Challenges and Stakeholder Perspectives in the Referral Process for Suspected Uveal Melanoma: A Cross-Sectional Mixed Methods Study
Bibliographic record
Abstract
Uveal melanoma (UM) is a rare but deadly eye cancer with a survival rate of 45% within 15 years of diagnosis (1). This is likely due to a lack of early treatment, a key characteristic of positive cancer outcomes (2). In fact, the risk of death due to UM increases by 1% for every 10-day delay in treatment (2). We surveyed and interviewed three major stakeholder groups in the UM referral process – ocular oncologists, primary eye care providers, and UM patients – to determine existing barriers in care that delay treatment. Ocular oncologists reported that many UM cases are referred too late, resulting in poor prognoses. They also identified a lack of information in referrals, leading to difficulties in triaging patients. Primary eye care providers lack confidence in differentiating between low- and high-risk lesions and are uncertain over where to send UM referrals. They stated that there is a lack of ocular oncologists needed to monitor suspicious lesions. Patients experienced initial misdiagnoses of their UM and described logistical barriers such as extensive travel and costly eye care that reduces accessibility of care. These challenges imply that there is a need for streamlining the UM referral process to achieve timely care.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".