Measuring the data gap: inclusion of sex and gender reporting in diabetes research
Bibliographic record
Abstract
BACKGROUND: Important sex and gender differences have been found in research on diabetes complications and treatment. Reporting on whether and how sex and gender impact research findings is crucial for developing tailored diabetes care strategies. To analyze the extent to which this information is available in current diabetes research, we examined original investigations on diabetes for the integration of sex and gender in study reporting. METHODS: We examined original investigations on diabetes published between January 1 and December 31, 2015, in the top five general medicine journals and top five diabetes-specific journals (by 2015 impact factor). Data were extracted on sex and gender integration across seven article sections: title, abstract, introduction, methods, results, discussion, and limitations. RESULTS: We identified 155 original investigations on diabetes, including 115 randomized controlled trials (RCTs) and 40 observational studies. Sex and gender were rarely incorporated in article titles, abstracts and introductions. Most methods sections did not describe plans for sex/gender analyses; 47 (30.3%) articles described plans to control for sex/gender in the analysis and 12 (7.7%) described plans to stratify results by sex/gender. While most articles (151, 97.4%) reported the sex/gender of study participants, only 10 (6.5%) of all articles reported all study outcomes separately by sex/gender. Discussion of sex-related issues was incorporated into 21 (13.5%) original investigations; however, just 1 (0.6%) discussed gender-related issues. Comparison by journal type (general medicine vs. diabetes specific) yielded only minor differences from the overall integration results. In contrast, RCTs performed more poorly on multiple sex/gender assessment metrics compared to observational studies. CONCLUSIONS: Sex and gender are poorly integrated in current diabetes original investigations, suggesting that substantial improvements in sex and gender data reporting are needed to inform the evidence to support sex- and gender-specific diabetes care.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.619 | 0.866 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".