MétaCan
Menu
Back to cohort
Record W7132940228

Menopause: Time for a Change

2014· other· en· W7132940228 on OpenAlexfundno aff
Cynthia Nathanson, Scott Allan, Nana Hou Liu

Bibliographic record

VenueTSpace · 2014
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersDepartment of Family and Community Medicine, University of Toronto
KeywordsNatural (archaeology)Process (computing)Degree (music)Transition (genetics)Life expectancy
DOInot available

Abstract

Menopause is not a disease; it is part of a woman’s natural life cycle. Defined as “the absence of menses for 12 months,” the average age of occurrence is between ages 48 and 52. The process usually begins four to six years earlier and continues for several years after. It is important to understand how each woman experiences menopause. How does she view herself in general and at this specific life stage? What kind of supports are in place within her family, her circle of friends, her physician, her community, and her culture? For some, the transition is easy and uneventful. However, up to 80% of all women will experience some degree of symptoms. 1 In this module, we examine the gaps between current practice and a more comprehensive approach to assisting patients and their families through menopause.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: fund_new · design weight: 1678.90 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: other
about Canada: no
confidence: high

Continuing-education module on menopause care; the object is clinical practice.

GPT-5.6 (high)OUT
genre: other
about Canada: no
confidence: high

This educational module concerns menopause care rather than research practice.

Grok 4.5OUT
genre: other
about Canada: no
confidence: high

Clinical teaching module on menopause care, not research methods or the research system.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.007
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0610.014

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 head0.038
GPT teacher head0.343
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueTSpaceFrench-language works237,207