Applicability of Retrospective and Prospective Gender Scores for Clinical and Health Data: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Gender is known to have a strong influence on human health and disease. Despite its relevance to treatment and outcome, gender is insufficiently considered in current health research. One hindering factor is the poor representation of gender information in clinical and health (meta) data. OBJECTIVE: We aim to conduct a scoping review of the literature describing gender scores. The review will provide insights into the current application of gender scores in clinical and health settings. The protocol describes how relevant literature will be identified and how gender scores will be evaluated concerning applicability and usability in scientific investigations. METHODS: Our scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A title and abstract screening was conducted on PubMed, followed by a full-text screening. The inclusion and exclusion criteria were discussed by a team of 5 domain experts, and a data-charting form was developed. The charted data will be categorized, summarized, and analyzed based on the research questions during the scoping review. RESULTS: We will report our research results according to the PRISMA-ScR guidelines. The literature retrieval was carried out on June 13, 2024, and resulted in 1202 matches. As of July 2024, the scoping review is in the data extraction phase and we expect to complete and publish the results in the first quarter of 2025. CONCLUSIONS: The scoping review lays the foundation for a retrospective gender assessment by identifying scores that can be applied to existing large-scale datasets. Moreover, it will help to formulate recommendations for standardized gender scores in future investigations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57669.
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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 | 0.162 | 0.189 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.062 | 0.018 |
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".