Sex and gender gap in spinal cord injury research: Focus on cardiometabolic diseases. A mini review
Bibliographic record
Abstract
Cardiometabolic disease (CMD) is among the leading causes of morbidity and mortality in people with a spinal cord injury (SCI). Despite well-acknowledged sex and gender differences in CMD in the general population, they remain insufficiently studied in persons with SCI. To describe the landscape of sex and gender in SCI research, we searched the literature for systematic reviews on cardiometabolic health in this population. Out of 15 systematic reviews identified, only 9 provided meaningful information on sex. Although one-quarter to one-fifth of the SCI population is female, women comprised only one-eighth to a quarter of study participants. A number of clinical studies purposively excluded women, to make the study population more homogenous. For those studies which included both sexes, in general, no sex-specific analyses were performed due to small sample sizes. All these reasons have contributed to the underrepresentation of females in the current body of evidence. Therefore, future studies should adopt a more sex- and gender-sensitive research framework to address cardiometabolic risk in SCI. Novel and advanced epidemiological methods should also be used, considering small sample sizes. Finally, collaborative research (through consortia and multi-center studies) should be encouraged to include more females. More inclusive research will ensure that everyone will benefit from scientific advancements, regardless of sex and gender.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".