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Record W2984810758 · doi:10.1186/s12883-019-1473-5

Age at menarche and risk of multiple sclerosis (MS): a systematic review and meta-analysis

2019· review· en· W2984810758 on OpenAlexaboutno aff
Amirreza Azimi, Sara Hanaei, Mohammad Ali Sahraian, Mehdi Mohammadifar, Sreeram V Ramagopalan, Mahsa Ghajarzadeh

Bibliographic record

VenueBMC Neurology · 2019
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMultiple sclerosisMeta-analysisNeurologyMenarcheNeurochemistryNeurosurgeryMEDLINEInternal medicinePsychiatryBiology

Abstract

fetched live from OpenAlex

Abstract Background Some studies have looked at the age at menarche and risk of Multiple Sclerosis (MS).We aimed to conduct a systematic review and meta-analysis to estimate a pooled odds ratio of developing MS by increasing age at menarche. Methods We searched PubMed, Scopus, EMBASE, CINAHL, Web of Science, Ovid, google scholar and gray literature (references of references, congress abstracts) up to 10th April 2019. Results The literature search found 312 articles. After eliminating duplicates, reviews, case reports and trials, 18 articles remained. Three articles were ultimately included in the final analysis. Two studies were from Iran, and one from Canada. The pooled odds ratio (OR) for increasing 1 year of age at menarche was 0.88 (95% CI:0.82-0.94), with no significant heterogeneity (I 2 = 49%, p = 0.1). Mean age at menarche was significantly different between case and control groups (mean difference = − 0.22, 95% CI = -0.42,-0.02). Conclusion The result of this systematic review showed that the risk of MS decreases by increasing age at menarche.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.842
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0180.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.383
GPT teacher head0.398
Teacher spread0.016 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2019
Admission routes1
Has abstractyes

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