Is there a measurable association of epidural use at cystectomy and postoperative outcomes? A population-based study
Bibliographic record
Abstract
INTRODUCTION: Thoracic epidural analgesia (TEA) is commonly used to manage postoperative pain and facilitate early mobilization after major intra-abdominal surgery. Evidence also suggests that regional anesthesia/analgesia may be associated with improved survival after cancer surgery. Here, we describe factors associated with TEA at the time of radical cystectomy (RC) for bladder cancer and its association with both short- and long-term outcomes in routine clinical practice. METHODS: All patients undergoing RC in the province of Ontario between 2004 and 2008 were identified using the Ontario Cancer Registry (OCR). Modified Poisson regression was used to describe factors associated with epidural use, while a Cox proportional hazards model describes associations between survival and TEA use. RESULTS: Over the five-year study period, 1628 patients were identified as receiving RC, 54% (n=887) of whom received TEA. Greater anesthesiologist volume (lowest volume providers relative risk [RR] 0.85, 95% confidence interval [CI] 0.75-0.96) and male sex (female sex RR 0.89, 95% CI 0.79-0.99) were independently associated with greater use of TEA. TEA use was not associated with improved short-term outcomes. In multivariable analysis, TEA was not associated with cancer-specific survival (hazard ratio [HR] 1.02, 95% CI 0.87-1.19; p=0.804) or overall survival (HR 0.91, 95% CI 0.80-1.03; p=0.136). CONCLUSIONS: In routine clinical practice, 54% of RC patients received TEA and its use was associated with anesthesiologist provider volume. After controlling for patient, disease and provider variables, we were unable to demonstrate any effect on either short- or long-term outcomes at the time of RC.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".