Quality of stepped-wedge trial reporting can be reliably assessed using an updated CONSORT: crowd-sourcing systematic review
Bibliographic record
Abstract
OBJECTIVES: The Consolidated Standards of Reporting Trials extension for the stepped-wedge cluster randomized trial (SW-CRT) is a recently published reporting guideline for SW-CRTs. We assess the quality of reporting of a recent sample of SW-CRTs. STUDY DESIGN AND SETTING: Quality of reporting was asssessed according to the 26 items in the new guideline using a novel crowd sourcing methodology conducted independently and in duplicate, with random assignment, by 50 reviewers. We assessed reliability of the quality assessments, proposing this as a novel way to assess robustness of items in reporting guidelines. RESULTS: Several items were well reported. Some items were very poorly reported, including several items that have unique requirements for the SW-CRT, such as the rationale for use of the design, description of the design, identification and recruitment of participants within clusters, and concealment of cluster allocation (not reported in more than 50% of the reports). Agreement across items was moderate (median percentage agreement was 76% [IQR 64 to 86]). Agreement was low for several items including the description of the trial design and why trial ended or stopped for example. CONCLUSIONS: When reporting SW-CRTs, authors should pay particular attention to ensure clear reporting on the exact format of the design with justification, as well as how clusters and individuals were identified for inclusion in the study, and whether this was done before or after randomization of the clusters, which are crucial for risk of bias assessments. Some items, including why the trial ended, might either not be relevant to SW-CRTs or might be unclearly described in the statement.
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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 | 0.732 | 0.899 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".