Comparison between complication rates of laser prostatectomy electrocautery transurethral resection of the prostate: A population-based study
Bibliographic record
Abstract
INTRODUCTION: We compare the complication rates and length of stay (LOS) of laser transurethral resection of the prostate (L-TURP) versus electrocautery transurethral resection of the prostate (E-TURP) in a population-based cohort. L-TURP has shown enhanced intraoperative safety and equivalent efficacy relative to E-TURP in several high volume centres. METHODS: Relying on the Florida Datafile as part of the Healthcare Cost and Utilization Project State Inpatient Databases (SID) between 2006 and 2008, we identified 8066 men with benign prostate hyperplasia who underwent L-TURP or E-TURP. Chi-square and Mann-Whitney tests were used to compare baseline characteristics. A multivariable linear regression model was used to analyze the effect of L-TURP versus E-TURP on complication rates and LOS. RESULTS: Overall complication rates did not differ significantly for L-TURP compared to E-TURP in univariable (8.8 vs. 7.4%, p = 0.1) and multivariable analyses (odds ratio [OR]: 1.06, confidence interval [CI]: 0.85-1.32, p = 0.6). Individuals undergoing E-TURP were less likely to experience a LOS in excess of 1 day (46.2 vs. 59.7%, p < 0.001). A lower risk to experience a LOS in excess of 1 day was confirmed for patients undergoing L-TURP after a multivariable linear regression model (OR: 0.37, CI: 0.23-0.58, p < 0.001), but not for a LOS in excess of 2 days (OR: 0.96, CI: 0.83-1.10, p = 0.2). CONCLUSIONS: Patient characteristics and perioperative safety were similar for L-TURP and E-TURP patients. However, LOS patterns demonstrated a modest benefit for L-TURP compared to E-TURP patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".