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Record W2760753042 · doi:10.1097/gme.0000000000000990

Age at natural menopause and its associated factors in Canada: cross-sectional analyses from the Canadian Longitudinal Study on Aging

2017· article· en· W2760753042 on OpenAlexafffundabout
Christy Costanian, Hugh McCague, Hala Tamim

Bibliographic record

VenueMenopause The Journal of The North American Menopause Society · 2017
Typearticle
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsYork University
FundersCanadian Institutes of Health Research
KeywordsMedicineMenopauseDemographyProportional hazards modelGerontologyLongitudinal studyHazard ratioDiseaseCohortInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Early onset of menopause is associated with long-term disease and higher mortality risks. Research suggests that age at natural menopause (ANM) varies across populations. Little is known about factors that affect ANM in Canadian women. OBJECTIVE: This study aims to estimate the median ANM and examine factors associated with earlier ANM among Canadian women. METHODS: Baseline data from the Tracking cohort of the Canadian Longitudinal Study on Aging was used for this analysis. The relation of sociodemographic, lifestyle, and health-related factors with ANM was examined among 7,719 women aged 40 and above. Nonparametric Kaplan-Meier cumulative survivorship estimates were used to assess the timing of natural menopause. Univariate and multivariate Cox proportional hazard regression models were used to characterize ANM and its association with relevant covariates. RESULTS: Overall, median ANM was 51 years. Having no partner, low household income and education levels, current and former smoking, and cardiovascular disease were all associated with an earlier ANM, whereas current employment, alcohol consumption, and obesity were associated with later ANM. CONCLUSIONS: These findings provide a national estimate of ANM in Canada and show the importance of lifestyle factors and health conditions in determining menopausal age. These factors might help in risk assessment, prevention and early management of chronic disease risk during the menopausal transition.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.089
GPT teacher head0.372
Teacher spread0.283 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations86
Published2017
Admission routes3
Has abstractyes

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