Reporting quality of stepped wedge design randomized trials: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Stepped wedge design (SWD) is a cluster randomized controlled trial (RCT) design that sequentially rolls out intervention to all clusters at varying time points. Being a relatively new design method, reporting quality has yet to be explored, and this review will seek to fill this gap in knowledge. OBJECTIVES: The objectives of this review are: 1) to assess the quality of SWD trial reports based on the CONSORT guidelines or CONSORT extension to cluster RCTs; 2) to assess the completeness of reporting of SWD trial abstracts using the CONSORT extension for abstracts; 3) to assess the reporting of sample size details in SWD trial reports or protocols; 4) to assess the completeness of reporting of SWD trial protocols according to SPIRIT guidelines; 5) to assess the consistency between the trial registration information and final SWD trial reports; and 6) to assess the consistency of what is reported in the abstracts and main text of the SWD trial reports. We will also explore factors that are associated with the completeness of reporting. METHODS: We will search MEDLINE, EMBASE, Web of Science, CINAHL, and PsycINFO for all randomized controlled trials utilizing SWD. Details from eligible papers will be extracted in duplicate. Demographic statistics obtained from the data extraction will be analyzed to answer the primary objectives pertaining to the reporting quality of several aspects of a published paper, as well as to explore possible temporal trends and consistency between abstracts, trial registration information, and final published articles. DISCUSSION: Findings from this review will establish the reporting quality of SWD trials and inform academics and clinicians on their completeness and consistency. Results of this review will influence future trials and improve the overall quality and reporting of SWD trials.
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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 | 0.432 | 0.527 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.021 | 0.024 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
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".