Are We Improving? Update and Critical Appraisal of the Reporting of Decision Process and Quality Measures in Trials Evaluating Patient Decision Aids
Bibliographic record
Abstract
BACKGROUND: In 2014, a systematic review found large gaps in the quality of reporting of measures used in 86 published trials evaluating the effectiveness of patient decision aids (PtDAs). The purpose of this study was to update that review. METHODS: We examined measures of decision making used in 49 randomized controlled trials included in the 2014 and 2017 Cochrane Collaboration systematic review of PtDAs. Data on development of the measures, reliability, validity, responsiveness, precision, interpretability, feasibility, and acceptability were independently abstracted by 2 paired reviewers. RESULTS: = 12). Very few studies reported data on the performance and clinical sensibility of measures, with reliability (23%) and validity (6%) being the most common. Studies using new measures were less likely to include information about their psychometric performance compared with previously published measures. LIMITATIONS: The review was limited to reporting of measures in studies included in the Cochrane review and did not consult prior publications. CONCLUSION: There continues to be very little reported about the development or performance of measures used to evaluate the effectiveness of PtDAs in published trials. Minimum reporting standards have been published, and efforts to require investigators to use them are needed.
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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 | 0.735 | 0.913 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.032 | 0.022 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.014 | 0.012 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".