Men Sustain Higher Dysregulation Levels Than Women Without Becoming Frail
Bibliographic record
Abstract
The aging process differs in important ways between the sexes, with women living longer but at higher risk for frailty (the male-female health-survival paradox). The underlying biological mechanisms remain poorly understood, but may relate to sex differences in physiological dysregulation patterns. Here, using biomarkers from two longitudinal cohort studies (InCHIANTI and BLSA) and one cross-sectional survey (NHANES), we assess sex differences in trajectories of dysregulation globally and for five physiological systems: oxygen transport, electrolytes, hematopoiesis, lipids, and liver/kidney function. We found higher dysregulation levels in men, both globally and in the oxygen transport and hematopoietic systems (p < .001 for all), though differences for other systems were mixed (electrolytes) or absent (lipids and liver/kidney). There was no clear evidence for sex differences in rates of change in dysregulation with age. Although risk of frailty and mortality increase with dysregulation, there was no evidence for differences in these effects between sexes. These findings imply that the greater susceptibility of women to frailty is not simply due to a tolerance for higher dysregulation; rather, it may actually be men that have a greater tolerance for dysregulation, creating a male-female dysregulation-frailty paradox. However, the precise physiological mechanisms underlying the sex differences appear to be diffuse and hard to pin down.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".