Bibliographic record
Abstract
Introduction Estrogens are frequently prescribed to perimenopausal and postmenopausal women for control of menopausal symptoms such as hot flashes, vaginal dryness, and memory disturbances. More recently, estrogens have been recognized and used for long-term protection against chronic diseases related to estrogen deficiency, most notably osteoporosis and heart disease. Estrogens have diverse multisystemic effects (Ettinger, 1998) including those on the central nervous system and have, therefore, been implicated in maintaining normal cognitive function and possibly reducing the risk of Alzheimer's disease (Henderson, 1997). Estrogens have also been linked to a reduced risk of colorectal cancer. Use of estrogens is limited, however, due to stimulatory effects on both the uterus and the breast, as well as some troublesome side-effects. In the uterus, there may be an increase in the risk of uterine cancer even when progestins are given appropriately (Beresford et al., 1997). Furthermore, the body of epidemiologic data suggests an increase in the risk of breast cancer, at least after long-term (>5–10 years') use (Collaborative Group, 1997). The increased risk of deep venous thrombosis has also been recently described in epidemiologic studies. Side-effects such as breast tenderness and engorgement, vaginal bleeding with many hormone replacement regimens, and a perception that hormone use is associated with weight gain, headaches, and nausea are other symptoms which limit estrogen use.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".