Postmenopausal estrogen replacement therapy and increased rates of cholecystectomy and appendectomy.
Bibliographic record
Abstract
BACKGROUND: Several studies have indicated that estrogen may prime inflammatory and nociceptive pathways, leading to symptoms that mimic cholecystitis. We set out to confirm the relation between recent estrogen use and cholecystectomy in postmenopausal women and to test the novel hypothesis that a similar relation exists for appendectomy. METHODS: We developed a retrospective cohort using prescribing and surgical procedure information from health administrative databases for approximately 800,000 female residents of Ontario who were over 65 years of age between July 1, 1993, and Mar. 31, 1998. We compared the incidence of cholecystectomy and appendectomy among women recently prescribed estrogen replacement therapy, levothyroxine and dihydropyridine calcium-channel antagonists (DCCA) using age-adjusted Cox proportional hazards models. Patients were followed for a mean of 540 (standard deviation [SD] 449) days. RESULTS: Compared with women taking DCCA, those who had recently begun taking estrogen were significantly more likely to undergo cholecystectomy (age-adjusted risk ratio [aRR] 1.9, 95% confidence interval [CI] 1.6-2.2) and appendectomy (aRR 1.8, 95% CI 1.1-3.0). No significant difference in either outcome measure was found between the levothyroxine users and the DCCA users. INTERPRETATION: This study identifies an increased risk of cholecystectomy and appendectomy among postmenopausal women who have recently begun estrogen replacement therapy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".