Bibliographic record
Abstract
BACKGROUND: Menopause is the permanent cessation of menstruation resulting from loss of ovarian follicular activity. The characteristic symptoms of a fall in oestrogen are vasomotor and urogenital atrophy symptoms; with symptoms reported by up to 85% of women over a mean duration of 5.2 years. Long term consequences of menopause include osteoporosis and cardiovascular disease. Menopause management is highly controversial and can be confusing for both clinicians and their women patients. OBJECTIVE: To explore menopausal management options including comprehensive evaluation; lifestyle modification for symptom relief and risk prevention; hormone therapy or nonhormonal alternatives for symptom relief; prevention and treatment of long term risks; and education and psychological support and therapy. DISCUSSION: Use of hormone therapy involves consideration of the woman's risk-benefit profile. We attempt to clarify this complex topic and focus on the impact of hormone therapy in women aged 50-59 years, including the benefits of relief of hot flushes and urogenital atrophy symptoms and the prevention of fractures and diabetes; and the risks, including venothrombotic episodes, stroke, cholecystitis and breast cancer (with combined oestrogen and progestogen only). Nonhormonal options are also explored.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".