Age of puberty and the risk of multiple sclerosis: a population based study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Genetic and environmental factors have important roles in multiple sclerosis (MS) susceptibility. Given a potential role for sex hormones in MS, we have investigated whether or not the age of puberty influences the risk of developing MS in a population-based cohort. METHODS: We identified 5493 MS index cases and 1759 spousal controls with age of puberty information from the Canadian Collaborative Project on Genetic Susceptibility to MS. Age of puberty was compared between index cases and controls, and any effect of age of puberty on the age of onset of MS was also investigated. RESULTS: There were no significant differences between male index cases and controls with respect to age of puberty, P = 0.70. However, a significant difference was observed between female index cases and female controls, with average age of puberty being 12.4 and 12.6 years respectively, P = 0.00017, providing a relative risk decrease of 0.9 per year increase of age of puberty. There was no effect of the age of puberty on the age of MS onset in either sex. CONCLUSIONS: Earlier age at menarche increases the risk of MS in women. Whether this association is a surrogate for a disease causative factor or directly involved in MS disease aetiology needs to be uncovered.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".