Patient-reported outcomes in randomized clinical trials: development of ISOQOL reporting standards
Bibliographic record
Abstract
PURPOSE: To develop expert consensus on a suite of reporting standards for HRQL outcomes of RCTs. METHODS: A Task Force of The International Society of Quality of Life Research (ISOQOL) undertook a systematic review of the literature to identify candidate reporting standards for HRQL in RCTs. Subsequently, a web-based survey was circulated to the ISOQOL membership. Respondents were asked to rate candidate standards on a 4-point Likert scale based on their perceived value in reporting studies in which HRQL was a study outcome (primary or secondary). Results were synthesized into draft reporting guidelines, which were further reviewed by the membership to inform the final guidance. RESULTS: Forty-six existing candidate standards for reporting HRQL results in RCTs were synthesized to produce a 40 item survey that was completed electronically by 161 respondents. The majority of respondents rated all 40 items to be either 'essential' or 'desirable' when HRQL was a primary RCT outcome. Ratings changed when HRQL was a secondary study outcome. Feedback on the survey findings resulted in the Task Force generalizing the guidance to include patient-reported outcomes (PROs). The final guidance, which recommends standards for use in reporting PROs generally, and more specifically, for PROs identified as primary study outcomes, was approved by the ISOQOL Board of Directors. CONCLUSIONS: ISOQOL has developed a suite of recommended standards for reporting PRO results of RCTs. Improved reporting of PROs will enable accurate interpretation of evidence to inform patient choice, aid clinical decision making, and inform health policy.
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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 | 0.821 | 0.884 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".