Health‐related behavior and the use of hormone replacement therapy
Bibliographic record
Abstract
PURPOSE: To study health-related differences between hormone replacement therapy (HRT) users and nonusers among colorectal cases and women without diagnosed cancer. METHODS: Data from the Saskatchewan Health population-based databases were used to ascertain the use of HRT, oral contraceptives (OCs), cardiovascular system (CVS) drugs, central nervous system (CNS) drugs, prescribed NSAIDs and vitamins among 3338 women diagnosed with colorectal cancer and 13025 women without diagnosed cancer. Physician visits and sigmoidoscopy procedures were also determined. RESULTS: Among women without diagnosed cancer, HRT was associated with CVS drugs (OR = 1.23, 95%CI: 1.10-1.37), CNS drugs (OR = 1.96, 95% CI: 1.72-2.23), other hormones (OR = 1.12, 95% CI: 1.01-1.24), prescribed vitamins (OR = 1.37, 95% CI: 1.22-1.55), NSAIDs (OR = 1.41, 95% CI: 1.18-1.68), having had a sigmoidoscopy 3-5 years prior to index dates (OR = 1.33, 95% CI: 1.12-1.59) and 15 or more visits to physicians during the 5th year prior to assigned index date (OR = 2.0, 95% CI: 1.77-2.35). Similar results were observed among women diagnosed with colorectal cancer, but HRT use was not associated with having had a sigmoidoscopy. CONCLUSIONS: Health-related characteristics of HRT users and nonusers are identified and described. Some of these factors may contribute to selection bias in studies examining the health benefits of HRT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".