Anterior cruciate ligament injury: towards a gendered environmental approach
Bibliographic record
Abstract
BACKGROUND: The anterior cruciate ligament (ACL) injury rate for girls/women has not changed in over 20 years, and they remain 3-6 times more likely to experience injury compared with boys/men. To date, ACL injury prevention and management has been approached from a sex-based biological point of view which has furthered our understanding of injury risk factors, mechanisms, and prevention and rehabilitation programmes. However, the traditional sex-based approach does not take into account the growing recognition of how sex and gender (a social construct) are 'entangled' and influence each other. OBJECTIVE: This paper discusses the curious absence of gender as an influencer in the dialogue surrounding ACL injuries. We propose adding gender as a pervasive developmental environment as a new theoretical overlay to an established injury model to illustrate how gender can operate as an extrinsic determinant from the presport, training and competition environments through to ACL injury and the treatment environment. APPROACH: We draw on social epidemiological theories of the embodiment of gender and health to provide plausible examples of how gender may influence ACL injury, and demonstrate the opportunity for new, interdisciplinary research in the field. CONCLUSION: Over 20 years of research has failed to decrease the ACL injury rate disparity between girls/women and boys/men. Embedding gender in the study of ACL injury will heighten awareness of possible influences outside the traditional biological elements, challenge us to think about the inextricable 'entanglement' of sex and gender, and inform more effective approaches to ACL injury prevention and treatment.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".