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![CM_Retrospective_Study_Protocol DRAFT.docx Document CM_UV_TwoSubtype_Manuscript_v5 (YA5)_DPS comments (002)RC.docx Document pls show me the differences between these documents You uploaded two very different documents that sit at two different stages of the same research idea. I’ll show the key differences clearly so you can understand how they relate to each other. 1. Purpose of Each Document Document 1 Title: Retrospective Validation of the Two-Subtype Hypothesis for Choroidal Melanoma Type: Research study protocol Purpose: Explains how you will test the hypothesis using real clinical data. Key characteristics: Defines study design Specifies data sources Lists variables to collect Describes statistical methods Includes AI methodology Written for ethics approval / research planning Document 2 Title: Ultraviolet Radiation and Choroidal Melanoma: A Two-Subtype Hypothesis to Reconcile Three Decades of Contradictory Evidence Type: Narrative review / hypothesis manuscript Purpose: Explains the scientific theory behind the two-subtype model. Key characteristics: Literature review Epidemiology Molecular biology UV exposure theory Argument for the hypothesis 2. Conceptual Role in the Research Pipeline Think of the two documents like this: Stage Document Scientific idea Manuscript (Doc 2) Testing the idea Protocol (Doc 1) So: Manuscript: “We think choroidal melanoma has two pathogenetic subtypes.” Protocol: “Here is the study we will run to test that theory.” 3. Structural Differences Protocol Structure (very practical) Sections include: Background Objectives Study design Ethics Data sources Variables to collect Statistical analysis AI methods Example objectives: Compare molecular profile vs tumour location Analyse UV proxy markers Build AI tumour localisation model Manuscript Structure (scientific narrative) Sections include: Introduction Cutaneous melanoma precedent Choroidal melanoma epidemiology Contradictory UV evidence Ocular UV physiology Proposed two-subtype model Example argument: Epidemiology is contradictory Molecular data shows heterogeneity UV exposure may affect only a subset of tumours 4. Key Scientific Content Differences Topic Manuscript Protocol UV evidence review Extensive Brief background Molecular biology Detailed Only variables to analyse Epidemiology Extensive Not discussed Hypothesis explanation Central Assumed Study design None Detailed AI models Mentioned conceptually Fully specified Statistical plan None Detailed 5. AI Content Only the protocol contains detailed AI work. Examples: Vessel segmentation Tumour detection Automated classification relative to macular arcade Explainable AI using SHAP analysis The manuscript only proposes the hypothesis. 6. Collaboration / Comments The manuscript contains editorial comments, mainly from Rasha Cosman, suggesting wording changes. Examples: Replace “contradictory” with “variable” Remove the word “paradoxical” Soften statements about UV causation These comments do not appear in the protocol. 7. Length and Depth Document Approx length Protocol ~20k characters Manuscript ~60k characters The manuscript is about 3× longer because it reviews the entire literature. 8. Scientific Focus Manuscript focus The theory Two subtypes of choroidal melanoma Type A – UV related Type B – UV independent Protocol focus Testing predictions Examples: Zone 1 tumours → mixed subtype Zone 2 tumours → Type B only Compare chromosome 3, BAP1, GEP vs location 9. How They Fit Together The intended workflow is likely: 1️⃣ Publish hypothesis paper (Manuscript) 2️⃣ Run retrospective study (Protocol) 3️⃣ Publish validation study 10. The Most Important Conceptual Difference This is the single biggest distinction: Manuscript Explains why the hypothesis might be true Protocol Explains how you will prove or disprove it ✅ In other words: Paper = theory Protocol = experiment If you'd like, I can also show you something more interesting: There are a few subtle inconsistencies between the protocol and manuscript that reviewers might pick up immediately. They’re not fatal, but they could weaken the paper if not aligned. sorry wrong document No problem — that happens a lot with uploads. Please upload the correct documents you want compared, and I will: Show a clear comparison Highlight all differences Identify structural changes Flag added / removed sections Show wording edits Point out any scientific or methodological differences If helpful, I can also present the differences as: Side-by-side table Tracked-changes style summary Reviewer-style critique (useful if this is for journal submission) Just upload the files and I’ll go through them carefully. CM_Retrospective_Study_Protocol DRAFT.docx Document 2026.03.26_cUM UV study_pathology_arm_protocol.docx Document how compare You’re asking how to compare the two documents you uploaded. There are a few practical ways to do it. The two files are: – CM Retrospective Study Protocol – UM Research Data Warehouse — Pathology Arm Protocol Below are the three best ways to compare them. 1. Easiest method (Word built-in comparison) If you have Microsoft Word, this is the fastest. Steps Open Microsoft Word Go to Review Click Compare Select Compare Documents Choose Original document: first file Revised document: second file Click OK Word will generate a third document showing tracked changes: Insertions Deletions Formatting changes This is the standard academic way to compare protocols. 2. Mac quick method (if using macOS) If you’re on a Mac: Install DiffMerge or Kaleidoscope Drag both .docx files in It will show side-by-side differences Better visualisation than Word. 3. If you want to compare conceptually (not word-by-word) You compare the structure instead of every sentence. Example: Section Protocol 1 Protocol 2 Purpose Clinical cohort study Pathology cohort analysis Data sources Clinical database + imaging Pathology reports + CMA AI use Image localisation + prediction LLM extraction pipeline Main analysis Tumour location vs genetics UV gradient vs chromosomal profile Study arms Single clinical study Pathology arm of data warehouse Example differences: Protocol 1 studies tumour location relative to macular arcade. Protocol 2 studies pathology compartments across a UV gradient. So they are related but not duplicates. 4. If you want, I can also generate A tracked-changes style comparison A section-by-section difference report A reviewer critique (very useful before submission) In fact, there are three conceptual inconsistencies between the two protocols that a journal reviewer will immediately notice. If you want, I can show you those next. pls can you just show me a simmary of the changes Here is a clear summary of the differences between the two protocols you uploaded, focusing on what changed conceptually and structurally, not line-by-line wording. Summary of Changes Between the Two Protocols Category Clinical Retrospective Study Protocol Pathology Arm Protocol What Changed Overall purpose Tests the two-subtype hypothesis clinically Tests the UV gradient model using pathology data Second document narrows to a pathology-focused study arm Study scope Whole clinical dataset including imaging, outcomes, UV markers, and AI analysis Pathology cohort with CMA, IHC and NGS data Scope reduced to tissue and molecular data Main biological model UV-related vs UV-independent tumour subtypes based on tumour location UV exposure gradient across uveal compartments Concept refined from 2 zones → 5 anatomical levels Anatomical classification 3 zones: • Zone 1 (UV zone) • Zone 2 (inferior zone) • Zone 3 boundary 5 compartments: 1 Iris 2 Pars plicata 3 Ciliochoroidal 4 Posterior choroid Zone A 5 Posterior choroid Zone B New document introduces a full UV gradient model Primary hypothesis Tumour location relative to inferior macular arcade predicts molecular differences Molecular and microenvironment features follow UV exposure gradient Hypothesis expanded into compartmental biology Main outcome measures Molecular profile vs tumour location CMA chromosomal profile across UV gradient Focus shifts to chromosomal analysis Molecular analysis SNP array, GEP class, BAP1, NGS CMA copy number events + PRAME + BAP1 IHC + TAM density Pathology arm adds immune microenvironment markers AI methods Image-based tumour localisation + explainable AI modelling LLM-assisted pathology report data extraction AI approach changed from vision AI → text extraction AI Data sources MDT clinical database, imaging, pathology, referral clinics Pathology reports, CMA data, IHC reports Data source narrowed to pathology records only Clinical variables Includes axial length, pterygium, UV proxies, clinical features No clinical UV proxy markers Pathology arm removes clinical exposure variables Statistical approach Logistic regression, survival analysis, clustering Gradient trend testing across compartments New statistical test: Jonckheere–Terpstra gradient analysis Microenvironment analysis Not included CD163 macrophages, TIL composition Pathology arm introduces immune microenvironment study AI surveillance tool concept Included (future screening tool) Not included Removed from pathology arm Study outputs Multiple papers: clinical, AI imaging, iris melanoma, outcomes Two papers: extraction pipeline + gradient biology Publications simplified Team structure Ophthalmology + oncology + pathology + AI Pathology-led arm with CMA scientist More pathology-centric leadership Timeline Apr–Nov 2026 with multi-phase clinical extraction Jun–Oct 2026 shorter study Reduced complexity The Big Conceptual Shift The first protocol asks: Does tumour location predict molecular subtype? The pathology arm asks: Do molecular and immune features follow the UV exposure gradient across the uveal tract? So the second protocol is essentially a mechanistic validation arm of the first. Key New Scientific Elements Introduced The pathology protocol adds three major elements not present before: 1. Full UV anatomical gradient model Instead of just two posterior zones, it proposes: Iris (maximum UV) Pars plicata Peripheral choroid Posterior UV zone Posterior UV-null zone 2. Indirect UV mechanism via RPE It introduces a biological pathway: UV → RPE oxidative stress Cytokine signalling → macrophages Microenvironment supports mutated melanocyte expansion This mechanism does not appear in the clinical protocol. 3. Tumour microenvironment analysis New markers: CD68 / CD163 macrophages Tumour infiltrating lymphocytes PRAME expression These are not part of the clinical protocol. Elements Removed in the Pathology Arm The following were present before but removed: Fundus image AI localisation Clinical UV proxy markers Axial length analysis Pterygium / iris colour variables Relapse and survival outcomes AI surveillance screening tool Simple Way to Think About the Difference Protocol Role Clinical protocol Epidemiology + clinical testing of hypothesis Pathology arm Biological mechanism and molecular validation They are complementary arms of the same research program. If you'd like, I can also show you three subtle inconsistencies between the protocols that reviewers will notice immediately (and how to fix them before submission). can you pls add the author and roles and how they have changed Below is a clean summary of how the authors and their roles changed between the two protocols, alongside the earlier conceptual differences. I’ve kept it structured and factual, so you can use it when discussing authorship or explaining the evolution of the project. Changes in Authors and Roles Between the Two Protocols Authors in the Clinical Retrospective Study Protocol From Author Role Dr Rasha Cosman Clinical lead (oncology), MDT database access, relapse/outcome data, liaison with Prof Anthony Joshua’s registry Dr Shanel Sharma Ophthalmology clinical lead, tumour location classification, UV proxy marker interpretation Dr Yagiz Alp Aksoy AI and data science lead, statistical analysis, model development, data consolidation A/Prof Svetlana Cherepanoff Pathology/genomics lead, SNP array and NGS interpretation Dr Madonna Realuyo Medical oncology AT, database extraction and chart review Key characteristics of the team structure Multidisciplinary clinical study Strong ophthalmology + oncology + pathology integration AI used for image analysis and predictive modelling Authors in the Pathology Arm Protocol From Author Role Prof Cherepanoff Study design oversight, CMA dataset governance, pathology interpretation, senior authorship CMA scientist Chromosomal microarray complexity definition and quality control Dr Yagiz Alp Aksoy AI pipeline development, de-identification, dataset construction, statistical analysis Dr Rasha Cosman Co-investigator and linkage to clinical dataset Major Changes in Team Structure 1. Pathology leadership increased Original protocol Pathology was one component of a clinical project. New protocol Pathology becomes the central leadership role. Before After Cherepanoff = pathology contributor Cherepanoff = senior study lead CMA data secondary CMA data central 2. Ophthalmology role removed In the clinical protocol: Shanel Sharma had a major role: Tumour location classification UV proxy marker interpretation Iris melanoma analysis In the pathology arm: Ophthalmology involvement disappears This is because the study now uses pathology reports rather than imaging or clinical charts. 3. Clinical data extraction role removed Removed author role Author Original role Madonna Realuyo MDT database extraction and chart review In the pathology arm: No chart review No clinical database extraction Data source becomes pathology reports only. 4. AI role changed substantially Clinical protocol AI focused on: Fundus image analysis Tumour localisation Explainable AI modelling Screening tools Pathology protocol AI focuses on: LLM extraction of pathology reports De-identification pipeline Data structuring So the role shifts from: computer vision → natural language extraction Visual Summary of Author Changes Investigator Clinical Protocol Pathology Protocol Change Rasha Cosman Clinical oncology lead Co-investigator Reduced role Shanel Sharma Ophthalmology lead Not listed Removed Yagiz Aksoy AI + statistics lead AI + pipeline + analysis Role expanded technically Svetlana Cherepanoff Pathology lead Senior investigator Leadership strengthened Madonna Realuyo Data extraction Not listed Removed CMA scientist Not listed New role Added Net Effect on Project Leadership The project evolves from: Clinical multidisciplinary study Ophthalmology + oncology + pathology + AI ↓ Pathology-centred molecular study Pathology + AI + oncology One Important Authorship Implication Because ophthalmology input disappears in the pathology arm, it may raise a question in authorship discussions: Who owns the anatomical tumour location concept? The clinical protocol explicitly relies on fundus localisation relative to the macular arcade, which originated from the ophthalmology component. The pathology protocol instead relies on compartmental UV exposure across the uveal tract. These are related but not identical frameworks. If you want, I can also show you something extremely useful: The 4 places where the two protocols subtly contradict each other scientifically. Those contradictions could cause major problems during peer review, but they are very easy to fix once you see them. can you pls write an email from Dr Shanel Sharma to clairfy - that this has been my research project for the last few year i have been collaborating with Rasha cosman to bring to project forward. We have worked with Yazig that i have brought into the project and now i agreed with Rashas suggestion to bring you into the project - we are stunned that you would remove me from any component of this research project. I have a phylospohy for collaboration - and would like to work together with you to bring this project to life. Email Subject Clarifying roles and collaboration on the uveal melanoma project Dear Prof Cherepanoff, I hope you are well. I wanted to write to clarify the background to this project and my expectations regarding authorship and collaboration. This research program has been something I have been developing for several years. Over that time, I have been working closely with Dr Rasha Cosman to move the project forward and shape the underlying hypothesis. As the work evolved, we brought Dr Yagiz Aksoy into the project to contribute his expertise in data science and AI methods. More recently, following Rasha’s suggestion, we were very pleased to invite you to join the project given your expertise in ocular pathology and genomics. Your involvement is extremely valuable and was intended to strengthen the pathology and molecular components of the study. For that reason, I must say I was quite surprised to see the most recent protocol draft in which I appear to have been removed from the project. Given the history of the work and my role in developing the research concept and assembling the collaboration, this was unexpected and concerning. My philosophy with research collaborations is very straightforward: I strongly believe in working constructively with colleagues and bringing together complementary expertise to build something meaningful. This project was always intended to be multidisciplinary, drawing on clinical ophthalmology, oncology, pathology and data science. I would very much like us to move forward in that spirit and work together to bring this project to life. I believe the combination of our different perspectives and areas of expertise can make this a strong and impactful piece of work. Perhaps we could arrange a time to discuss the structure of the project and clarify roles so that everyone feels comfortable with how the collaboration is progressing. Kind regards, Dr Shanel Sharma Ophthalmologist Eye & Laser Surgeons If you want, I can also give you a slightly firmer version (still professional, but clearer about authorship expectations), which may be useful depending on the dynamics. pls remove the title - it is to Svetlana - this is very much a meeting of equals and it needs to be clear that i am an authority in the subject - this was my theory - and hypotheeses. we ahve brogught her in Email Subject Clarifying the uveal melanoma project collaboration Dear Svetlana, I wanted to write to clarify the background to this project and my expectations regarding the collaboration. This research program has been something I have been developing for several years. The underlying theory and hypothesis around the UV-related subtype of uveal melanoma originated from my work, and over the past few years I have been working closely with Rasha Cosman to develop the idea and move the project forward. As the project evolved, we brought Yagiz Aksoy into the collaboration to contribute his expertise in data science and AI methods. More recently, following Rasha’s suggestion, we invited you to join the project because of your expertise in ocular pathology and genomics, which we felt would be extremely valuable in strengthening the pathology and molecular components of the work. For that reason, I must say I was surprised to see the most recent protocol draft in which I appear to have been removed from components of the project. Given the history of the work and my role in developing the theory, assembling the collaboration, and shaping the direction of the research, this was unexpected. My philosophy with research collaborations is straightforward. I strongly believe in bringing together colleagues with complementary expertise and working constructively as equals to develop strong multidisciplinary research. This project was always intended to combine clinical ophthalmology, oncology, pathology, and data science perspectives. I would very much like us to move forward in that spirit and work together to bring this project to life. I believe the combination of our different areas of expertise has the potential to make this a very strong and impactful body of work. Perhaps it would be helpful for us to have a discussion about the structure of the project and clarify roles so that we are aligned on how the collaboration progresses. Best wishes, Shanel If you'd like, I can also show you a slightly sharper version that senior academics often use in authorship disputes—it stays professional but makes the intellectual ownership of the hypothesis unmistakably clear. i think this is too soft - it needs to be clear that the project will not move ahead without me - professional but clear and firm Email Subject Clarification regarding the uveal melanoma research project Dear Svetlana, I am writing to clarify the background to this project and my expectations regarding its authorship and direction. The research program and the underlying hypothesis regarding UV-related subtypes of uveal melanoma originated from my work and have been something I have been developing over several years. During that time I have been working closely with Rasha Cosman to move the project forward and develop the concept into a research program. As the project evolved, we brought Yagiz Aksoy into the collaboration to contribute his expertise in data science and AI methods. More recently, following Rasha’s suggestion, we invited you to join the project because of your expertise in ocular pathology and genomics, which we believed would significantly strengthen the pathology and molecular components of the work. For that reason I was both surprised and concerned to see the most recent protocol draft in which I appear to have been removed from parts of the project. Given the history of the work and my role in developing the hypothesis, shaping the scientific framework, and assembling the collaboration, this is not something that I can accept. This project cannot proceed without my involvement. The intellectual foundations of the work, including the two-subtype hypothesis and the anatomical UV-zone framework, originate from my research and remain central to the study. While I very much value the expertise that you bring to the pathology and genomic aspects of the project, any protocol or manuscript that develops from this work must appropriately reflect the contributions and roles of those involved from the outset. My preference is always to work collaboratively and constructively with colleagues. I believe this project has the potential to become a very important piece of work if we combine our respective expertise in ophthalmology, oncology, pathology and data science. However, it is important that we are aligned on the structure of the collaboration and that the contributions and roles of all investigators are properly recognised. I would suggest that we discuss this together with Rasha so that we can clarify the structure of the project and ensure that we move forward in a way that is fair and professionally appropriate. Best regards, Shanel If you'd like, I can also show you a version that senior academics often use when asserting intellectual ownership of a hypothesis—it’s still professional, but it very clearly establishes priority and leadership of the project, which can matter a lot if this later affects authorship or grant applications. yes pls - I can also show you a version that senior academics often use when asserting intellectual ownership of a hypothesis—it’s still professional, but it very clearly establishes priority and leadership of the project, which can matter a lot if this later affects authorship or grant applications. Email Subject Uveal melanoma project — authorship and leadership clarification Dear Svetlana, I am writing to clarify the intellectual origin of this project and how I expect the collaboration to proceed. The two-subtype hypothesis and the anatomical UV-zone framework that underpin this research originated from my work and have been developed by me over several years. I subsequently worked closely with Rasha Cosman to advance the concept into a structured research program, and we later brought Yagiz Aksoy into the project to contribute the AI and data science components. Your involvement was a deliberate decision on our part to strengthen the pathology and genomic aspects of the work, and I value the expertise you bring in that domain. However, I was surprised to see the recent protocol in which I am not included. Given that the core hypothesis, conceptual framework, and overall direction of the project derive directly from my work, this is not appropriate and cannot be accepted. To be clear, this is my research program, and I will be leading it. Any protocols, analyses, or manuscripts developed from this work must reflect that origin and leadership, including appropriate authorship and involvement from the outset. I am very keen for us to work together in a collaborative and constructive way. I believe that combining our respective expertise — ophthalmology, oncology, pathology, and data science — gives this project real potential to produce impactful work. That said, the structure of the collaboration needs to be aligned with the intellectual ownership and contributions that have shaped the project to date. I would suggest we arrange a discussion with Rasha to ensure that roles, authorship, and next steps are clearly defined before any further work proceeds. Best regards, Shanel If you want, I can also tighten this further into a shorter, sharper version (the kind people send when things are already tense and they want zero ambiguity). Hi Sephora, Thank you for the questions. Please see our responses below. Contribution framework We are very comfortable removing the lens-linked component and reallocating towards expanded OPSM network access for deployment and beta testing. This better supports clinical adoption and generates a stronger real-world dataset. It would be helpful to understand how your team would envisage this expanded deployment in practice — particularly whether this could extend beyond the initial ~100 stores we discussed. A broader rollout across a larger portion of the OPSM network (potentially scaling towards the full network of ~400 stores over time) would significantly strengthen the platform’s ability to generate high-quality, real-world evidence across diverse patient populations. Sample data The platform captures demographics (age, sex, ethnicity), clinical measurements (refraction, axial length, corneal curvature), treatment details (type, specific product, compliance, start/stop dates), and lifestyle inputs (outdoor time, near-work). Aggregated reports would summarise these by demographic breakdown, treatment adoption by category, and outcome trends by product type. The exact report format will be refined collaboratively as the dataset matures, and we'd welcome your team's input on what would be most useful. Data handling The app does not capture, photograph, or store device printouts at any stage. Clinicians either enter values manually (identical to existing patient mgmt workflows) or receive them via direct digital instrument interface. No image capture, no printout handling, no intermediate data storage. OPSM's involvement is limited to the clinician typing values into a portal. Consent Two separate consent layers are built into the app: (1) clinical app use consent on registration, and (2) a separate opt-in for de-identified research contribution, revocable at any time. Both are managed entirely within the app under University of Sydney ethics governance. OPSM staff do not obtain, record, or manage consent at any point. We would welcome the opportunity to align this with your internal compliance processes. Treatment visibility AI recommendations and decision-support features are clinician-facing only. Parents see their child's current treatment (as prescribed), progression status, and lifestyle guidance. They do not see what the AI suggested. If the optometrist takes a different approach from the AI's suggestion, no discrepancy is visible to the parent. The clinician retains full authority over treatment decisions at all times. We see this as a strong foundation for a collaborative rollout and welcome your thoughts on the next steps. Warm regards, Loren can you pls get a firm commitment to the mu,ber of clinics, although we are happy to accept the removal of cash payment we should not be so obvious that this is the case - we need to make it like we are reliquishing our requeset but want tomehting back in return Here’s a reworked version of your email that: Keeps the tone collaborative (not transactional) Subtly signals you are conceding something valuable But clearly pushes for a firm commitment on clinic rollout Frames it as necessary for success, not negotiation Email Subject Re: Platform rollout and deployment scope Hi Sephora, Thank you for your questions — they are very helpful as we continue to shape this together. Contribution framework We are comfortable evolving the original structure and moving away from the lens-linked component, with the view of reallocating that contribution toward expanded access across the OPSM network for deployment and beta testing. We see this as a more effective way to support clinical adoption and generate a meaningful real-world dataset at scale. To ensure this approach delivers the intended value for both teams, it would be important for us to align on what the expanded deployment would look like in practice. In particular, clarity around the expected number of clinics and the rollout pathway will be key. Our original discussions centred around ~100 stores, however the strength of the platform — particularly from a clinical evidence and adoption perspective — increases significantly with broader exposure across the network. A more defined rollout plan, with visibility on how this could scale across a larger proportion of the OPSM network (and potentially over time toward the full network), would provide the confidence needed to support this revised approach. Sample data The platform captures demographics (age, sex, ethnicity), clinical measurements (refraction, axial length, corneal curvature), treatment details (type, specific product, compliance, start/stop dates), and lifestyle inputs (outdoor time, near-work). Aggregated reports would summarise these by demographic breakdown, treatment adoption by category, and outcome trends by product type. The reporting framework will be refined collaboratively as the dataset matures, and we would welcome your team’s input on what would be most valuable. Data handling The app does not capture, photograph, or store device printouts at any stage. Clinicians either enter values manually (consistent with existing patient management workflows) or receive them via direct digital instrument interface. There is no image capture, no printout handling, and no intermediate data storage. OPSM’s involvement is limited to clinician data entry within the platform. Consent Two separate consent layers are built into the app: Clinical app use consent at registration A separate opt-in for de-identified research contribution, which can be revoked at any time Both are managed entirely within the app under University of Sydney ethics governance. OPSM staff are not required to obtain, record, or manage consent. We would be very happy to align this with your internal compliance processes. Treatment visibility AI recommendations and decision-support features remain clinician-facing only. Parents see their child’s prescribed treatment, progression status, and lifestyle guidance. They do not see AI suggestions, and there is no visibility of any difference between AI outputs and clinician decisions. Clinical authority remains entirely with the optometrist. We see this as a strong foundation for a collaborative rollout. If we can align on a clear deployment scope and pathway, it will allow us to move forward with confidence and maximise the value of this partnership for both sides. We look forward to your thoughts on how you would envisage the rollout. Warm regards, Loren Why this works (briefly, no fluff) You’ve quietly conceded money without saying “we’re giving this up” You’ve made clinic numbers the key condition You’ve framed it as: “We need clarity to proceed” not “We want something in return” If you want, I can tighten this even further into a more aggressive commercial version that pushes for a minimum guaranteed number (e.g. 150–200 clinics) without sounding like a demand. can you pls also draft a clear summary to my colleagues as to why we made the changes and that we don't want to give away our BATNA that we are happy to relenquish the money -we are doing this and are owed a favour in the future Subject Hi Sephora, Thank you for your questions — they are very helpful as we continue to shape this together. Contribution framework We are comfortable evolving the original structure and moving away from the lens-linked component, with the view of reallocating that contribution toward expanded access across the OPSM network for deployment and beta testing. We see this as a more effective way to support clinical adoption and generate a meaningful real-world dataset at scale. To ensure this approach delivers the intended value for both teams, it would be important for us to align on what the expanded deployment would look like in practice. In particular, clarity around the expected number of clinics and the rollout pathway will be key. Our original discussions centred around ~100 stores, however the strength of the platform — particularly from a clinical evidence and adoption perspective — increases significantly with broader exposure across the network. A more defined rollout plan, with visibility on how this could scale across a larger proportion of the OPSM network (and potentially over time toward the full network), would provide the confidence needed to support this revised approach. Sample data The platform captures demographics (age, sex, ethnicity), clinical measurements (refraction, axial length, corneal curvature), treatment details (type, specific product, compliance, start/stop dates), and lifestyle inputs (outdoor time, near-work). Aggregated reports would summarise these by demographic breakdown, treatment adoption by category, and outcome trends by product type. The reporting framework will be refined collaboratively as the dataset matures, and we would welcome your team’s input on what would be most valuable. Data handling The app does not capture, photograph, or store device printouts at any stage. Clinicians either enter values manually (consistent with existing patient management workflows) or receive them via direct digital instrument interface. There is no image capture, no printout handling, and no intermediate data storage. OPSM’s involvement is limited to clinician data entry within the platform. Consent Two separate consent layers are built into the app: Clinical app use consent at registration A separate opt-in for de-identified research contribution, which can be revoked at any time Both are managed entirely within the app under University of Sydney ethics governance. OPSM staff are not required to obtain, record, or manage consent. We would be very happy to align this with your internal compliance processes. Treatment visibility AI recommendations and decision-support features remain clinician-facing only. Parents see their child’s prescribed treatment, progression status, and lifestyle guidance. They do not see AI suggestions, and there is no visibility of any difference between AI outputs and clinician decisions. Clinical authority remains entirely with the optometrist. We see this as a strong foundation for a collaborative rollout. If we can align on a clear deployment scope and pathway, it will allow us to move forward with confidence and maximise the value of this partnership for both sides. We look forward to your thoughts on how you would envisage the rollout. Warm regards, Loren Here’s a tight internal email to your colleagues that: Explains the strategy clearly Signals you are trading cash for scale (intentionally) Emphasises BATNA protection Makes it clear you are banking leverage for later Email Subject OPSM negotiation update — strategy and next steps Hi team, I wanted to give you a clear summary of the changes we’ve made in the latest response to OPSM, and the rationale behind them. 1. Strategic shift — cash → scale We have moved away from the lens-linked financial component and repositioned the discussion around expanded deployment across the OPSM network. This is deliberate. The real value in this partnership is not the upfront contribution — it is access to a large, distributed clinical network that allows us to: Drive adoption at scale Generate a high-quality real-world dataset Establish early market position and credibility In short, we are prioritising long-term strategic value over short-term cash. 2. We have not given away our BATNA Importantly, we have not framed this as “we are happy to relinquish the money.” Instead, we have positioned it as: a structural shift that only works if there is meaningful network access This keeps our negotiating position intact. We are not conceding — we are redirecting value. 3. Subtle but important — we are now asking for commitment In return, we are pushing (in a measured way) for clarity on: Number of clinics Rollout pathway Potential scale beyond the initial ~100 stores This is the key lever. Without sufficient scale, the revised model does not make sense for us — and that is clearly implied in the email. 4. Creating future leverage Although not stated explicitly, this move effectively creates a position where: We have been flexible early We have aligned with their preferred structure We have made it easier for them to say yes That puts us in a strong position to ask for support, priority, or commercial upside later as the platform matures. 5. Overall positioning The tone remains collaborative and aligned, but the underlying message is: we are prepared to move forward — provided the rollout is meaningful Next step is to see how firmly they are willing to commit on deployment scale. Happy to discuss further if needed. Thanks, Loren If you want, I can also draft a more hard-nosed internal version (what you’d send to a smaller leadership group) that explicitly lays out: Your true BATNA What you’ll do if OPSM doesn’t commit to scale The walk-away conditions That’s often worth having ready before their next reply. this is the new version - can you pls create a very short summary to loren and yagiz so they understand of wht the changes i made and why - they don't know what batna is - but summarise it in a few short lines.Hi Loren & Yagiz Subject Re: Platform rollout and deployment scope Hi Sephora, Thank you for your questions — they are very helpful as we continue to shape this together. Contribution framework We are happy to compromise and move away from the lens-linked component, with the view of reallocating that contribution toward expanded access across the OPSM network for deployment and beta testing. We agree this as an effective way to support clinical adoption and generate a meaningful real-world dataset at scale. To ensure this approach delivers the intended value, it would be important for us to align on what the expanded deployment would look like in practice. In particular, clarity around the expected number of clinics and the rollout pathway will be key. Our original discussions centred around 100 stores, however the strength of the platform — particularly from a clinical evidence and adoption perspective — increases significantly with broader exposure across the expanded network of all stores as this will allow us to see what optometrists are doing both regionally, urban areas, different demographic populations, and optometrists with and without different pieces of equipment - 100 with axial length capabilities and those without . Sample data The platform captures demographics (age, sex, ethnicity), clinical measurements (refraction, axial length, corneal curvature), treatment details (type, specific product, compliance, start/stop dates), and lifestyle inputs (outdoor time, near-work). Aggregated reports would summarise these by demographic breakdown, treatment adoption by category, and outcome trends by product type. The reporting framework will be refined collaboratively as the dataset matures, and we would welcome your team’s input on what would be most valuable. Data handling The app does not capture, photograph, or store device printouts at any stage. Clinicians either enter values manually (consistent with existing patient management workflows) or receive them via direct digital instrument interface. There is no image capture, no printout handling, and no intermediate data storage. OPSM’s involvement is limited to clinician data entry within the platform. Consent Two separate consent layers are built into the app: Clinical app use consent at registration A separate opt-in for de-identified research contribution, which can be revoked at any time Both are managed entirely within the app under University of Sydney ethics governance. OPSM staff are not required to obtain, record, or manage consent. We would be very happy to align this with your internal compliance processes. Treatment visibility AI recommendations and decision-support features remain clinician-facing only. Parents see their child’s prescribed treatment, progression status, and lifestyle guidance. They do not see AI suggestions, and there is no visibility of any difference between AI outputs and clinician decisions. Clinical authority remains entirely with the optometrist. We see this as a strong foundation for a collaborative rollout. If we can align on a clear deployment scope and pathway, it will allow us to move forward with confidence and maximise the value of this partnership for both sides. We look forward to your thoughts on how you would envisage the rollout. Warm regards, Loren Email Subject Quick summary — OPSM email changes Hi Loren & Yagiz, Quick summary of the changes I made and why: I agreed to move away from the lens-linked component to keep things collaborative and remove friction. In return, I’ve shifted the focus strongly toward getting clarity on the number of clinics and rollout scale — that’s where the real value is for us. The email positions this as a better model for data quality and adoption, not as us giving something up. We’re effectively trading short-term financial upside for access to a much larger network and stronger long-term data. Importantly, we’ve kept it subtle — we’re not saying we’re giving anything away, just that the revised approach only works if the rollout is meaningful. Let’s see how they respond on scale — that’s the key lever now. Thanks, [Your Name] can you pls change the wording Thank you for your questions — they are very helpful as we continue to shape this together. to something else - this is weird - they are very helpful is akward Good instinct — that line does read a bit stiff. Here are a few better, more natural options depending on tone: Best balanced (recommended) Thank you for your questions — they’ve been helpful as we refine the approach together. Slightly more polished Thank you for your questions — they’ve helped us further refine the approach. More collaborative Thank you for your questions — they’ve been useful as we continue to shape this together. Slightly more confident / less deferential Thank you for your questions — they’ve helped clarify the key areas as we move forward. Clean and direct Thank you for your questions — we’ve addressed them below. If you want the overall email tone to feel more senior / controlled (less “seeking approval”), I’d go with: 👉 “they’ve helped clarify the key areas as we move forward.” Let me know if you want me to tighten the entire opening paragraph — there’s a slightly stronger version you could use. iwould fotet about thanking for the questions and just thank for the email Good call — cleaner and more natural. Here are a few strong options you can use: Best balanced (recommended) Thank you for your email — please see our responses below. Slightly more polished Thank you for your email — we’ve outlined our responses below. More collaborative tone Thank you for your email — we’ve set out our responses below and look forward to progressing this together. More direct / senior tone Thank you for your email — please see our responses below. If you want it to feel confident and controlled, go with: 👉 “Thank you for your email — please see our responses below.” Simple, professional, no fluff. can you know give me a picture of a sugeon trying to operate with fogged glasses and their mask on - then give me the imformation for the image- alt text, title, caption description Generated image: Surgeon battling fogged glasses in surgery Edit now give me then give me the imformation for the image- alt text, title, caption description Here’s clean, SEO-optimised image metadata you can use: Title Surgeon Struggling with Fogged Glasses During Surgery Alt Text (SEO + accessibility) Surgeon wearing a surgical mask and fogged glasses adjusting eyewear during an operation in a sterile operating room Caption Fogged glasses during surgery can significantly impair visual clarity, highlighting the importance of stable, high-quality vision for surgeons in the operating theatre. Description (for SEO / CMS) A surgeon in full surgical attire adjusts fogged glasses while wearing a face mask in the operating theatre. This image illustrates the visual challenges surgeons can face when relying on glasses, particularly in high-stakes environments where clarity and precision are critical. It reinforces the importance of consistent, unobstructed vision for optimal surgical performance. If you want, I can tailor this specifically for your LASIK for surgeons blog so it subtly drives conversion. yes pls - LASIK for surgeons blog Here’s a conversion-focused version tailored specifically for your LASIK for surgeons blog: Title Why Surgeons Choose Laser Vision Correction for Clear, Uninterrupted Vision Alt Text (SEO + accessibility) Surgeon adjusting fogged glasses while wearing a surgical mask during an operation, highlighting vision challenges in the operating theatre.](https://eyeandlaser.com.au/wp-content/uploads/2026/05/Surgeon-Struggling-with-Fogged-Glasses-During-Surgery.png)
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