Gender‐related protection from or vulnerability to severe CNS diseases: Gonado‐structural and/or gonado‐activational? A meta‐analysis of relevant epidemiological studies
Bibliographic record
Abstract
BACKGROUND: A vast scientific literature has dealt with gender-specific risk for brain disorder. That field is evolving toward a consensus to the effect that the estrogen hormone family is outstandingly and uniquely neuroprotective. However, the epidemiology relevant to this general outlook remains piecemeal. METHOD: The present investigation strategically formats the relevant epidemiological findings around the world in order to quantitatively meta-analyze gender ratio of risk for a variety of relevant severe central nervous system (CNS) diseases at all three gonadal stages of the life cycle, pre pubertal, post adolescent/pre menopausal, and post menopausal. RESULTS: The data quantitatively establish that (1) no single epidemiological study should be cited as evidence of gender-specific neuroprotection against the most common severe CNS diseases because the gender-specific risk ratios are contradictory from one study to the other; (2) risk for severe CNS disease is indeed significantly gender-specific, but either gender can be protected: it depends on the disease, not at all on the age bracket. CONCLUSION: Our assay of gender-specific risk for severe brain disease around the world has not been able to support the idea according to which any one gender-prevalent gonadal steroid hormone dominates as a neuroprotective agent at natural concentrations.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".