Reporting of cluster randomised crossover trials: extension of the CONSORT 2010 statement with explanation and elaboration
Bibliographic record
Abstract
This article presents the CONSORT (consolidated standards of reporting trials) extension for cluster randomised crossover trials. A cluster randomised crossover trial involves randomisation of groups of individuals (known as clusters) to different sequences of interventions over time. The design has gained popularity in settings where cluster randomisation is required because it can largely overcome the loss in power due to clustering in parallel cluster trials. However, the design has many methodological complexities, requiring tailored reporting guidance. The guideline was developed using a survey and in-person consensus meeting, informed by a systematic review examining the quality of reporting in cluster randomised crossover trials and relevant CONSORT statements for individual, crossover, cluster, and stepped wedge designs. This article also provides recommended reporting items, along with explanations and examples.
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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 | 0.499 | 0.727 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".