F.4 Relationship between poor postoperative pain control and surgical outcomes after elective spine surgery
Bibliographic record
Abstract
Background: Inadequate pain control after spine surgery is common, but its impact on long-term surgical outcomes has not been studied. Accordingly, this study aimed to investigate the relationship between poor postoperative pain control and surgical outcomes. Methods: Consecutive adult patients undergoing elective spine surgery were enrolled. Poor surgical outcome was defined as failure to achieve a minimal clinically important difference (MCID) of 30% improvement on the Oswestry Disability Index or Neck Disability Index at follow-up (3-months, 1-year, 2-years). Poor pain control was defined as a mean numeric rating scale score of >4 within 24-hours postoperatively. Univariable analyses followed by multivariable random-effects models were used, after adjusting for known risk factors that impact surgical outcomes. Results: 42.8% of 1305 patients failed to achieve MCID at follow-up. 56.9% had poor postoperative pain control. Poor pain control was independently associated with failure to achieve MCID (OR 2.15 [95%CI=1.42-3.25], p<0.001), after adjusting for age (p=0.15), sex (p=0.59), PHQ-9 score (p=0.030), ASA physical status >2 (p<0.001), ≥3 motion segment surgery (p=0.003), revision surgery (p=0.032), and follow-up time (p<0.001). Conclusions: Poor pain control 24-hours after elective spine surgery was an independent risk factor for poor surgical outcome. Perioperative strategies to improve pain control may lead to improved outcomes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".