The State of Patient-Reported Outcome Measures in Rheumatology
Bibliographic record
Abstract
Objective We sought to evaluate the quality and timeliness of patient-reported outcome (PRO) measure reporting, which have not been previously studied. Methods Clinical trials that informed new US Food and Drug Administration (FDA) approvals for the first rheumatological indication between 1995 and 2021 were identified. Data were recorded to determine whether collected PROs were published, met minimum clinically important difference (MCID) or statistical significance (P< 0.05) thresholds, and were consistent with Consolidated Standards of Reporting Trials (CONSORT)-PRO standards. Hazard ratios and Kaplan-Meier estimate were used to assess the time from FDA approval to PRO publication. Results Thirty-one FDA approvals corresponded with 110 pivotal trials and 262 reported PROs. Of the 90 included studies, 1 (1.1%) met all 5 recommended items, 10 (11.1%) met 4 items, 17 (18.9%) met 3 items, 21 (23.3%) met 2 items, 26 (28.9%) met 1 item, and 15 (16.7%) met none of the reporting standards. Most PROs met MCID thresholds (149/262; 56.9%) and were statistically significant (223/262; 85.1%). Of our subset analysis, one-third of PROs were not published upfront (70/212; 33%) and 1 of 9 (22/212; 10.4%) remained unpublished ≥ 4 years after initial trial reporting. Publication rates were highest for the Health Assessment Questionnaire–Disability Index (97.4%) and lowest for the 36-item Short Form Health Survey (81.8%). Less than half of these published PROs met MCID and statistical significance thresholds (94/212; 44.3%). Conclusion One in 9 PROs remained unpublished for ≥ 4 years after initial trial reporting, and compliance with CONSORT-PRO reporting guidelines was poor. Efforts should be made to ensure PROs are adequately reported and expeditiously published.
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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 | 0.461 | 0.670 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".