Reporting of Sex and Gender in Randomized Controlled Trials of Rehabilitation Treated Distal Radius Fractures: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To determine the extent to which sex and gender are considered in the design and reporting of distal radius fracture rehabilitation randomized controlled trials (RCTs). DATA SOURCES: PubMed, Embase, CINAHL, and Pedro databases were searched in March 2022, and an updated search was conducted in July 2023. STUDY SELECTION: All RCTs with a rehabilitation intervention and any comparison were included. DATA EXTRACTION: We extracted information on the study characteristics and sex and gender reporting in the articles. We extracted whether the studies complied with the sex and gender equity in research (SAGER) guidelines and a reporting tool for sex and gender. DATA SYNTHESIS: A total of 77 studies were included in this review. All studies were published between 1987 and 2021. Two were in children, and the rest were in adults. This systematic review found that sex and gender were adequately considered in only 6 of the 77 RCTs investigating rehabilitation interventions after distal radius fracture. Three of those studies were published before the SAGER guidelines were published in 2016, and 3 were published after 2016. CONCLUSIONS: Overall, sex and gender were inadequately defined, and poorly addressed in the study design, conduct, and interpretation. Unfortunately, there was no evidence of improvement after 2016 when the SAGER guidelines became available.
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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 | 0.156 | 0.499 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".