Complications after surgery for benign prostatic enlargement: a population-based cohort study in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: To examine the complication rates after benign prostatic enlargement (BPE) surgery and the effects of age, comorbidity and preoperative medical therapy. DESIGN: A retrospective, population-based cohort study using linked administrative data. SETTING: Ontario, Canada. PARTICIPANTS: 52 162 men≥66 years undergoing first BPE surgery between 1 January 2003 to 31 December 2014. INTERVENTION: Medical therapy preoperatively and surgery for BPE. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was overall 30-day postoperative complication rates. Secondary outcomes included BPE-specific event rates (bleeding, infection, obstruction, trauma) and non-BPE specific event rates (cardiovascular, pulmonary, thromboembolic and renal). Multivariable analysis examined the association between preoperative medical therapy and postoperative complication rates. RESULTS: The 30-day overall complication rate after BPE surgery was 2828 events/10 000 procedures and increased annually over the study period. Receipt of preoperative α-blocker monotherapy (relative rate (RR) 1.05; 95% CI 1.00 to 1.09; p=0.033) and antithrombotic medications (RR 1.27; 95% CI 1.22 to 1.31; p<0.0001) was associated with increased complication rates. Among the ≥80-year-old group, the rate of complications increased by 39% from 2003 to 2014 (RR 1.39; 95% CI 1.21 to 1.61; p<0.0001). The mean duration of medical and conservative management increased by a mean of 2.1 years between 2007 and 2014 (p<0.0001 for trend). CONCLUSIONS: Thirty-day complication rates after BPE surgery have increased annually between 2003 and 2014. Preoperative medical therapy with alpha blockers or antithrombotics was independently associated with higher rates of complications. Over this time, the duration of conservative therapy also increased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".