Examining the impact of postoperative opioid use on length of hospital stay following radical cystectomy
Bibliographic record
Abstract
INTRODUCTION: Radical cystectomy (RC) is associated with high rates of morbidity, prolonged hospital stay, and increased opioid use for postoperative pain management; however, the relationship between postoperative opioid use and length of stay (LOS ) remains uncharacterized. This study serves to investigate the association between postoperative opioid use and length of hospital stay after RC. The relationship between patient and surgical factors on LOS was also characterized. METHODS: We retrospectively reviewed all patients between 2009 and 2019 who underwent RC at our institution. Patient and perioperative variables were analyzed to determine the relationship between postoperative opioid use and LOS using multivariable linear regression analysis. RESULTS: We identified 240 patients for study inclusion with a median age of 70.0 years. Median LOS was 10.0 days, with median daily mg morphine equivalent use of 57.5 for patients. Daily mg morphine equivalent use was significantly associated with an increased LOS, as were previous pelvic radiation, postoperative ileus, and higher Clavien-Dindo grade complication during admission (all p<0.05). Median LOS increased by one day for each increase of 13.2 daily mg morphine equivalents received. CONCLUSIONS: Increased daily opioid use was associated with increased length of hospital stay after RC. Non-opioid-based pain management approaches may be effective in reducing LOS after 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".