Enhanced recovery after cystectomy in patients with preoperative narcotic use
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to evaluate the outcomes of radical cystectomy with an enhanced recovery after surgery (ERAS) protocol in patients with a history of chronic preoperative narcotic use compared to narcotic-naive patients. METHODS: We identified 553 patients who underwent open radical cystectomy with ERAS. Preoperative narcotic use was identified in 34 patients who were then matched to 68 narcotic-naive patients. Postoperative outcomes, opioid use, and visual analog scale (VAS) pain scores were analyzed and compared. All routes of opioid use were recorded and converted to a morphine equivalent dose (MED). RESULTS: Patients with preoperative narcotic use reported higher median VAS pain scores per day (postoperative day [POD1]: 5.2 vs. 3.9, p=0.003; POD2: 5.1 vs. 3.6, p<0.001; POD3: 4.6 vs. 3.8, p=0.004) and used significantly more opioids (median MED) per day (POD1: 13.2 vs. 10.0, p=0.02; POD2: 11.3 vs. 6.4, p=0.003; POD3: 10.2 vs. 5.0, p=0.005) following surgery. Preoperative narcotic users were noted to have a significantly higher incidence of 90-day re-admissions (41.2% vs. 20.6%, p=0.03). There was no difference in median hospital stay (4 vs. 4 days, p=0.6), 30-or 90-day complications (64.7% vs. 60.3%, p=0.8 and 82.4% vs. 75.0%, p=0.4, respectively) or gastrointestinal complications (29.4% vs. 26.5%, p=0.8), including postoperative ileus (11.8% vs. 20.6%, p=0.2). CONCLUSIONS: Patients with preoperative narcotic exposure report higher pain scores and require more opioid use following radical cystectomy with ERAS and are more likely to be re-admitted within 90 days. However, there was no observed difference in hospital stay or complications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".