Development and validation of a patient based measure of outcome in ocular melanoma
Bibliographic record
Abstract
BACKGROUND: Patients with uveal melanoma can be treated by a number of modalities. As none of the different treatments offer a survival advantage, a key factor in choosing among treatments is their differential impact on patients' quality of life. A short, patient based questionnaire was developed and validated for evaluating outcomes following treatment for uveal melanoma. METHODS: The 21 item measure of outcome in ocular disease (MOOD) assesses the patient's view of outcome in terms of visual function and the impact of treatment. The reliability and validity of the three MOOD scores (total, vision, impact) were evaluated in 176 patients who had been treated for uveal melanoma (75 brachytherapy, 78 proton beam radiotherapy, 23 enucleation). Of these, 165 patients also completed the SF-36. RESULTS: All three MOOD scales met standard criteria for acceptability, reliability, and validity. The proportion of missing data was low, and responses to all items were well distributed across response categories. Internal consistency, assessed by Cronbach's alpha coefficients, exceeded the standard criterion of 0. 70 for all three summary scores. Item total correlations ranged from 0.22 to 0.77 (mean item total correlation 0.58), indicating good homogeneity. Test-retest correlations for all three summary scores exceeded 0.85. Scaling assumptions, assessed by item convergent and discriminant validity correlations, were met for the vision and impact scores. The MOOD showed good content validity, as assessed by review by ophthalmologists and patients. Construct validity was demonstrated by high intercorrelations between the vision and impact scores and the total scale; higher scores for patients who reported being very satisfied compared with those who were not very satisfied and for those who reported persistent red eye compared with those who did not have this complication (known group differences/hypothesis testing); moderate correlations between the MOOD and the SF-36 and visual acuity (convergent validity); and low correlations between the MOOD and age and sex (discriminant validity). CONCLUSIONS: The MOOD is a practical and scientifically sound patient based measure which can be used in research and audit to evaluate outcomes following treatment for uveal melanoma. It takes 5 minutes to complete and meets standard psychometric criteria for reliability and validity.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".