Sex differences in musculoskeletal injury and disease risks across the lifespan: Are there unique subsets of females at higher risk than males for these conditions at distinct stages of the life cycle?
Bibliographic record
Abstract
Sex differences have been reported for diseases of the musculoskeletal system (MSK) as well as the risk for injuries to tissues of the MSK system. For females, some of these occur prior to the onset of puberty, following the onset of puberty, and following the onset of menopause. Therefore, they can occur across the lifespan. While some conditions are related to immune dysfunction, others are associated with specific tissues of the MSK more directly. Based on this life spectrum of sex differences in both risk for injury and onset of diseases, a role for sex hormones in the initiation and progression of this risk is somewhat variable. Sex hormone receptor expression and functioning can also vary with life events such as the menstrual cycle in females, with different tissues being affected. Furthermore, some sex hormone receptors can affect gene expression independent of sex hormones and some transitional events such as puberty are accompanied by epigenetic alterations that can further lead to sex differences in MSK gene regulation. Some of the sex differences in injury risk and the post-menopausal disease risk may be "imprinted" in the genomes of females and males during development and sex hormones and their consequences only modulators of such risks later in life as the sex hormone milieu changes. The purpose of this review is to discuss some of the relevant conditions associated with sex differences in risks for loss of MSK tissue integrity across the lifespan, and further discuss several of the implications of their variable relationship with sex hormones, their receptors and life events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".