Bibliographic record
Abstract
Perimenopausal women often consult health care professionals for help in managing vasomotor symptoms such as hot flashes and night sweats. Similar symptoms are also associated with the use of certain drugs such as tamoxifen and leuprolide.1,2 Various therapies have been studied for the treatment of these symptoms, the most predominant being hormonal therapy (HT). Because of the risks and contraindications associated with HT, however, nonhormonal pharmacologic therapy has been explored for the treatment of hot flashes. Antidepressants (e.g., venlafaxine, paroxetine and fluoxetine), as well as clonidine, Bellergal (belladonna, ergotamine and phenobarbital) and gabapentin are medications that can be prescribed as alternatives to HT, as suggested by the Society of Obstetricians and Gynaecologists of Canada (SOGC).3 While antidepressants affect the release and reuptake of serotonin and/or norepinephrine,4-6 gabapentin and pregabalin are gamma-aminobutyric acid analogues,7,8 and their mechanism of action related to the reduction of hot flashes is currently unclear. Patients using gabapentin for neurologic conditions have been described in the literature as having a reduction in hot flashes.9 Although pregabalin is not listed by the SOGC as one of the options for treating hot flashes, preliminary data supporting its use for this indication are available.10,11 This investigational use of pregabalin stems from past evidence that shows the benefit of gabapentin,12,13 an agent of the same class, for the treatment of hot flashes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".