The use of herbal medicines for the treatment of menopausal symptoms in general practice.
Bibliographic record
Abstract
Hormone therapy (HT) is the most effective treatment for some symptoms (eg. hot flushes, dry vagina) associated with menopause. However, its popularity has recently been challenged following adverse publicity related to side effects. Use of complementary and alternative medicines (CAM) including herbal medicines (HM) has grown exponentially. In the USA and Canada, up to 70% of menopausal women use CAM. But we do not understand the demographics, health status, health service utilisation and perceptions of CAM users which might help inform general practitioners, particularly in Australia. Although we have data on the prevalence of HM use in the community, it is combined with other CAM such as vitamins, minerals and homeopathic medicines. Nor is there research data on the use of HM for menopausal symptoms. This may be especially important because of potential drug-herb interactions8 and the fact that the majority of patients do not inform their GPs of CAM use. We set out to address these gaps.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".