Study of the Association Between Menarche and Disease Course in Pediatric Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sex steroid hormones have been demonstrated to affect the immune system in multiple sclerosis (MS), and puberty may trigger MS activity. We aimed to evaluate the association between menarche and disease course in pediatric MS through comparison of relapse rates across premenarche, perimenarche, and postmenarche periods. METHODS: This is a retrospective analysis of a prospectively followed female cohort with pediatric-onset MS in the US Network of Pediatric MS Centers database. Perimenarche was considered the period from 1 year before to 1 year after the estimated menarche date based on menarche integer age. Relapses were collected prospectively. Negative binomial and repeated-measures Cox regression models were used to assess the association of pubertal development stage with relapse rate, adjusted for race, body mass index, and disease-modifying therapy (DMT). RESULTS: < 0.001). DISCUSSION: Onset of puberty may be a time of increase in disease activity and may require consideration of a change in therapeutic approach. Menarche age was used as a surrogate for puberty, and future studies measuring sex steroid hormones may be informative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".