Systematic Evaluation of Patient-Reported Outcome Protocol Content and Reporting in Cancer Trials
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes (PROs) are captured within cancer trials to help future patients and their clinicians make more informed treatment decisions. However, variability in standards of PRO trial design and reporting threaten the validity of these endpoints for application in clinical practice. METHODS: We systematically investigated a cohort of randomized controlled cancer trials that included a primary or secondary PRO. For each trial, an evaluation of protocol and reporting quality was undertaken using standard checklists. General patterns of reporting where also explored. RESULTS: Protocols (101 sourced, 44.3%) included a mean (SD) of 10 (4) of 33 (range = 2-19) PRO protocol checklist items. Recommended items frequently omitted included the rationale and objectives underpinning PRO collection and approaches to minimize/address missing PRO data. Of 160 trials with published results, 61 (38.1%, 95% confidence interval = 30.6% to 45.7%) failed to include their PRO findings in any publication (mean 6.43-year follow-up); these trials included 49 568 participants. Although two-thirds of included trials published PRO findings, reporting standards were often inadequate according to international guidelines (mean [SD] inclusion of 3 [3] of 14 [range = 0-11]) CONSORT PRO Extension checklist items). More than one-half of trials publishing PRO results in a secondary publication (12 of 22, 54.5%) took 4 or more years to do so following trial closure, with eight (36.4%) taking 5-8 years and one trial publishing after 14 years. CONCLUSIONS: PRO protocol content is frequently inadequate, and nonreporting of PRO findings is widespread, meaning patient-important information may not be available to benefit patients, clinicians, and regulators. Even where PRO data are published, there is often considerable delay and reporting quality is suboptimal. This study presents key recommendations to enhance the likelihood of successful delivery of PROs in the future.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".