Describing deprescribing trials better: an elaboration of the CONSORT statement
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to identify key features to be addressed in the reporting of deprescribing trials and to elaborate and explain CONSORT items in this regard. STUDY DESIGN AND SETTING: As a first step in a multistage process and based on a systematic review of deprescribing trials, we elaborated variation in design, intervention, and reporting of the included trials of the review. We identified items that were missed or insufficiently described, using the CONSORT and TIDieR checklists. The resulting list of items, which we considered relevant to be reported in deprescribing trials, were discussed in a single-round Delphi exercise and subsequently in a full-day face-to-face meeting with an international panel of 14 experts. We agreed on CONSORT items for further elaboration with regard to design and reporting of deprescribing trials. RESULTS: We identified seven CONSORT items on trial design, participants, intervention, outcomes, flowchart, and harms, where the investigators of deprescribing trials should take into consideration specific aspects, such as whether or not to use placebo or how to inform participants. CONCLUSION: This article presents an elaboration to the CONSORT statement for the reporting of deprescribing trials. It may also support investigators in motivated design choices.
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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 | 0.762 | 0.818 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".