The CONSORT Patient-Reported Outcome (PRO) extension: implications for clinical trials and practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To inform clinical guidelines and patient care we need high quality evidence on the relative benefits and harms of intervention. Patient reported outcome (PRO) data from clinical trials can "empower patients to make decisions based on their values" and "level the playing field between physician and patient". While clinicians have a good understanding of the concept of health-related quality of life and other PROs, evidence suggests that many do not feel comfortable in using the data from trials to inform discussions with patients and clinical practice. This may in part reflect concerns over the integrity of the data and difficulties in interpreting the results arising from poor reporting.The new CONSORT PRO extension aims to improve the reporting of PROs in trials to facilitate the use of results to inform clinical practice and health policy. While the CONSORT PRO extension is an important first step in the process, we need broader engagement with the guidance to facilitate optimal reporting and maximize use of PRO data in a clinical setting. Endorsement by journal editors, authors and peer reviewers are crucial steps. Improved design, implementation and transparent reporting of PROs in clinical trials are necessary to provide high quality evidence to inform evidence synthesis and clinical practice guidelines.
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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 | 0.031 | 0.429 |
| 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 it