Use of patient-reported outcomes in ophthalmology clinical trials between 2014 and 2023
Bibliographic record
Abstract
BACKGROUND/AIMS: Patient-reported outcomes (PROs) evaluate health and functional status, and PRO measures (PROMs) are standardised tools for measuring PROs. Together, they provide valuable insights into treatment efficacy, safety and practicality not captured by traditional clinical endpoints. This cross-sectional analysis with a systematic search component aims to investigate the use, interpretation and reporting of PROs and PROMs in ophthalmic randomised controlled trials (RCTs). METHODS: Ophthalmic RCTs published in the top 10 highest impact factor ophthalmic and medical journals between 2014 and 2023 were systematically reviewed. The frequency of PRO inclusion and adherence to Consolidated Standards of Reporting Trials (CONSORT) PRO guidelines was assessed. The relationship between PRO utilisation and study-level and journal-level characteristics was explored with multivariable regression. RESULTS: Among 9436 records screened, 333 RCTs met eligibility criteria. Of these, 87 (26.1%) included PROs, and 28 (8.4%) used them as primary outcomes. Most studies (83/87, 95.4%) leveraged PROMs, with ophthalmology-specific tools predominating (73.5%). Minimal important differences (MIDs) were rarely used (2.3%) for PRO interpretation. At least 8/13 CONSORT PRO Extension items were reported in 33.3% studies, and trials with primary PRO endpoints had better adherence (p<0.001). PRO utilisation was less likely in trials with lower 5-year journal impact factor (adjusted OR (aOR) 0.99, 95% CI 0.98 to 1.00, p=0.037) and pharmaceutical compared with health service interventions (aOR 0.15, 95% CI 0.03 to 0.67, p=0.013). CONCLUSIONS: PRO integration and interpretation remain limited in high-impact ophthalmic RCTs, despite offering a meaningful adjunct to objective endpoints. Future trials should adopt validated and condition-specific PROMs and establish MIDs to enhance interpretation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".