A scoping review on the current state of sex- and gender-based analysis (SGBA) in standing balance research
Bibliographic record
Abstract
BACKGROUND: Understanding sex and gender differences in standing balance is challenged by varied use of terminology and definitions. In addition, the use of sex- and gender-based analyses (SGBA) in standing balance research is unknown. This scoping review examined the frequency and type of SGBA, and the use of sex- and gender-based terminology in standing balance research published in the year 2020. METHODS: Eight databases were searched for peer-reviewed articles that quantitatively measured standing balance in adult humans using a biomechanical construct and were published in 2020. Two independent reviewers screened abstracts and extracted data with a third reviewer resolving conflicts. In accordance with sex and gender equity in research (SAGER) guidelines, data extraction focused on participant demographics, inclusion and type of SGBA, consistency of sex and gender terminology, alignment with operational definitions (e.g., female used to describe sex), and sex and gender data collection methods. Absolute and relative values across all articles and within collaboratively created categories of participant groups were calculated. RESULTS: Of the 366 articles in the analysis, 20 % included sex and/or gender in the statistical analyses of which 50 % conducted SGBA. Consistent terminology aligned with this study's definitions of sex and gender was found in 12 % of all articles, whereas 40 % used labels consistently without assigning them to sex or gender, ∼20 % used inconsistent or unaligned terminology, and 7 % did not report sex or gender. No articles included more than two options for sex or gender, and very few included self-reporting by participants (3 % for sex, 1 % for gender) or clearly described how sex (3 %) or gender (1 %) data were collected. CONCLUSIONS: Small changes to the collection and reporting of sex and gender, and more SGBA in standing balance research could drastically improve the inclusivity and accuracy of standing balance assessment in research and clinical settings.
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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 | 0.073 | 0.282 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.036 | 0.033 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".