Knowledge translation concerns for the CONSORT-PRO extension reporting guidance: a review of reviews
Bibliographic record
Abstract
This review of reviews aimed to appraise the use of the CONSORT-PRO Extension as an evaluation tool for assessing the reporting of patient-reported outcome (PROs) in publications, and to describe the reporting of PRO research across reviews. We also outlined how variation in such evaluations impacts knowledge translation and may lead to potential misuse of the CONSORT-PRO Extension. We systematically searched Medline, Pubmed and CINAHL from 2013 to 2025 March 2021 for reviews of the completeness of reporting of PRO endpoints according to CONSORT-PRO criteria. Two reviewers extracted details of each review, the percentage of included studies that addressed each CONSORT-PRO item, and key recommendations from each review. Fourteen reviews met inclusion criteria, and only six of these used the full CONSORT-PRO checklist with minimal justified modifications. The remaining eight studies made significant or unjustified adjustments to the CONSORT-PRO Extension. Review studies also varied in how they scored multi-component CONSORT-PRO items. CONSORT-PRO items were often unreported in trial reports, and certain CONSORT-PRO items were reported less often than others. The reporting of statistical approaches to dealing with missing PRO data were poor in RCTs included in all 14 review articles. Studies reviewing PRO publications often omitted recommended CONSORT-PRO items from their evaluations, which may cause confusion among readers regarding how best to report their PRO research according to the CONSORT-PRO extension. Many trials published since CONSORT-PRO's release did not report recommended CONSORT-PRO items, which may lead to misinterpretation and consequently to research waste.
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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 | 0.320 | 0.354 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads 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".