P.150 Effect of postoperative pain control and other perioperative risk factors on length of stay after elective spine surgery
Bibliographic record
Abstract
Background: Prolonged length of stay (LOS) after elective spine surgery increases patients’ risk for in-hospital complications and contributes significantly to healthcare costs. Here we explored the role of pain control and other perioperative factors on LOS. Methods: Consecutive adult patients undergoing elective spine surgery were enrolled. The primary outcome was in-hospital LOS following surgery. The primary independent variable was poor pain control on postoperative day 1 (POD1). Univariable analyses followed by multivariable regression analysis were used to investigate the relationship between poor pain control and LOS. Results: 1305 patients were enrolled. Mean LOS was 4.38 days. Incidence of poor pain control was 56.9%. Multivariable analysis revealed poor POD1 pain control was significantly associated with increased LOS (p=0.03), after adjusting for other significant predictors of increased LOS including perioperative hemodynamic instability (p=0.001), perioperative blood transfusion (p=0.000), delirium (p=0.000), POD1 morphine equivalent dose (p=0.000), urinary tract infection (p=0.000), urinary retention (p=0.003), surgical site infection (p=0.000), wound complication (p=0.000), neurologic deterioration (p=0.000), surgical levels (p=0.016), operative time (p=0.007), ASA score (p=0.000), preoperative disability score (p=0.001). Conclusions: Poor pain control on POD1 was an independent predictor of increased LOS after elective spine surgery, highlighting the importance of a proactive approach to addressing pain in the immediate postoperative period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".