The Randomized Controlled Trials Rehabilitation Checklist
Bibliographic record
Abstract
BACKGROUND: One of the goals of Cochrane Rehabilitation is to strengthen methodology relevant to evidence-based clinical practice. Toward this goal, several research activities have been performed in rehabilitation literature: a scoping review listed the methodological issues in research, a study showed the low clinical replicability of randomized controlled trials, two systematic reviews showed the relevant items in reporting guidelines, and a series of articles discussed main methodological issues as a result of the first Cochrane Rehabilitation Methodological Meeting (Paris 2018). The need to improve the quality of conduct and reporting of research studies in rehabilitation emerged as a relevant task. The aim of this article is to present the Randomized Controlled Trial Rehabilitation Checklists (RCTRACK) project to produce a specific reporting guideline in rehabilitation. METHODS: The project followed a combination of the CONsolidated Standards of Reporting Trials and EQUATOR Network methodologies. The project includes five phases. The first is kick-off, first consensus meeting and executive and advisory committee identification. The second is literature search and synthesis, where eight working groups will produce knowledge synthesis products (systematic or scoping reviews) to compile items relevant to reporting of randomized controlled trials in rehabilitation. The topics will be as follows: patient selection; blinding; treatment group; control group and co-interventions; attrition, follow-up, and protocol deviation; outcomes; statistical analysis and appropriate randomization; and research questions. The third is guidelines development, which means drafting of a document with the guidelines through a consensus meeting. The fourth is Delphi process consensus, a Delphi study involving all the rehabilitation research and methodological community. The fifth is final consensus meeting and publication. CONCLUSIONS: The RCTRACK will be an important contribution to the rehabilitation field and will impact several groups of rehabilitation stakeholders worldwide. The main goal is to improve the quality of the evidence produced in rehabilitation research. The RCTRACK also wants to improve the recognition and understanding of rehabilitation within Cochrane and the scientific and medical community at large.
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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 | 0.475 | 0.591 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.014 | 0.012 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.045 | 0.016 |
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".