Patient perspectives on dry eye disease and chronic ocular surface pain: Insights from a virtual community-moderated dialogue
Bibliographic record
Abstract
PURPOSE: To understand patients' perspectives on living with dry eye disease (DED), and on the unmet needs in DED and chronic ocular surface pain (COSP) management. METHODS: A moderated, structured discussion with patients with ocular surface diseases and healthcare professionals (HCPs) was conducted using a virtual platform to capture patients' journey with DED, their opinion on unmet needs, and design and conduct of clinical trials in DED and COSP. RESULTS: Nine participants, including four patient representatives from patient organisations, one ophthalmologist and one optometrist participated in the discussion. Patients had DED of varying severity and aetiology; three patients had Sjögren's. Over 4 weeks, 785 posts were entered on the platform. Prior to diagnosis, patients rarely associated their symptoms with DED. Convenience and symptomatic relief scored higher than treating the disease. Patients expressed the need for plain language information and dialogue with knowledgeable and sensitive HCPs. Online forums and social media were suggested as key recruitment resources, whereas convenience and safety concerns were highlighted as main barriers to enrolment. The need for the inclusion of outcome measures that have a real impact on patients' experience of their condition was highlighted. Both target product profiles were received positively by participants, highlighting the twice-daily dosing regimen and convenience of the products. Participants acknowledged the value of digital tools and suggested the need to feel valued post-trial. CONCLUSIONS: This moderated dialogue provided actionable insights on the unmet needs in DED and useful inputs for consideration when designing future clinical trials for DED and COSP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".