The many menopauses: searching the cognitive research literature for menopause types
Bibliographic record
Abstract
OBJECTIVE: Recent evidence suggests that early or induced menopause increases the risk for cognitive impairment and dementia. Given the potential for different cognitive outcomes due to menopause types, it is important that present research on menopause and cognition distinguishes between types. The aim of this project was to determine to what extent research looking at cognition in postmenopausal women published in one year, 2016, accounted for menopausal type. METHODS: We searched MEDLINE, EMBASE, and PsychINFO using keywords and MeSH terms for menopause and cognition. We included any research paper reporting a cognitive outcome measure in a menopausal human population. Differentiation between the types of menopause was defined by four categories: undifferentiated, demographic differentiation (menopause type reported but not analyzed), partial differentiation (some but not all types analyzed), and full differentiation (menopause types factored into analysis, or recruitment of only one type). RESULTS: Fifty research articles were found and analyzed. Differentiation was distributed as follows: undifferentiated, 38% (19 articles); demographic differentiation, 16% (8); partial differentiation, 28% (14); and full differentiation, 18% (9). CONCLUSIONS: This review revealed that although some clinical studies differentiated between the many menopauses, most did not. This may limit their relevance to clinical practice. We found that when menopause types are distinguished, the differing cognitive outcomes of each type are clarified, yielding the strongest evidence, which in turn will be able to inform best clinical practice for treating all women.
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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".