Bibliographic record
Abstract
Perimenopause, rather than a time of declining estrogen, is characterized by three major hormonal changes that may begin in regularly menstruating women in their mid-thirties: erratically higher estradiol levels, decreased progesterone levels (in normally ovulatory, short luteal phase or anovulatory cycles), and disturbed ovarian-pituitary-hypothalamic feedback relationships. Recent data show that approximately a third of all perimenopausal cycles have a major surge in estradiol occurring de novo during the luteal phase. This phenomenon, named "luteal out of phase (LOOP)" event, may explain a large proportion of symptoms and signs for symptomatic perimenopausal women. Large urinary hormone data-sets from women studied yearly over a number of years in the Study of Women Across the Nation (SWAN) and in the Tremin data will eventually provide a more clear prospective understanding of within-woman hormonal changes. Predicting menopause proximity with FSH or Inhibin B levels is documented to be ineffective. Anti-Mullerian hormone levels may prove predictive. Finally, there is an urgent need to change perimenopause understandings, language and therapies used for midlife women's symptoms to reflect these hormonal changes.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".