Furosemide use and hospitalization for benign prostatic hyperplasia.
Bibliographic record
Abstract
OBJECTIVE: Recent studies have shown that furosemide may have anti-inflammatory properties. We explored whether exposure to furosemide would reduce the risk of being hospitalized with prostatism, a marker of benign prostatic hyperplasia. METHODS: Using record linkage and the computerized health insurance databases of the province of Québec, Canada, we identified a cohort of men 65 years of age and older within which we conducted a case-control study. Cases were individuals hospitalized with prostatism (ICD-9 code 600) between January 1991 and June 1993, with the index date taken as the date of hospitalisation. Controls were those not having experienced the event during the study period, with an index date selected randomly during their follow-up. Cases and controls were required to have at least 2 (1/2) years of health coverage prior to index date in order to identify risk factors for benign prostatic hyperplasia and establish baseline medical history. We assessed the subjects' exposure to furosemide and various other diuretics in the period 180 to 900 days preceding the index date. Logistic regression was used to evaluate the association between the use of furosemide and hospitalization for prostatism, adjusting for potential confounders. RESULTS: The cohort included 8,814 subjects, of which 231 were cases and 8,583 controls. The rate of hospitalization for prostatism was lower for users of furosemide compared to non-users (adjusted rate ratio 0.49; 95% CI: 0.25-0.95). There was no association with the use of thiazide or potassium sparing diuretics (adjusted rate ratio 0.95; 95% CI: 0.65-1.37). Results suggestive of a protective effect associated with corticosteroid use were observed (adjusted rate ratio 0.64; 95% CI: 0.44-0.93). CONCLUSIONS: This study supports the hypothesis that furosemide can reduce the risk of hospitalization for prostatism, a marker of benign prostatic hyperplasia.
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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 | 0.003 |
| 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".