Poor postoperative pain control is associated with poor long-term patient-reported outcomes after elective spine surgery: an observational cohort study
Bibliographic record
Abstract
BACKGROUND CONTEXT: A significant proportion of patients experience poorly controlled surgical pain and fail to achieve satisfactory clinical improvement after spine surgery. However, a direct association between these variables has not been previously demonstrated. PURPOSE: To investigate the association between poor postoperative pain control and patient-reported outcomes after spine surgery. STUDY DESIGN: Ambispective cohort study. PATIENT SAMPLE: Consecutive adult patients (≥18-years old) undergoing inpatient elective cervical or thoracolumbar spine surgery. OUTCOME MEASURE: 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, and 2-years). METHODS: Poor pain control was defined as a mean numeric rating scale score of >4 during the first 24-hours after surgery. Multivariable mixed-effects regression was used to investigate the relationship between poor pain control and changes in surgical outcomes while adjusting for known confounders. Secondarily, the Calgary Postoperative Pain After Spine Surgery (CAPPS) Score was investigated for its ability to predict poor surgical outcome. RESULTS: Of 1294 patients, 47.8%, 37.3%, and 39.8% failed to achieve the MCID at 3-months, 1-year, and 2-years, respectively. The incidence of poor pain control was 56.9%. Multivariable analyses showed poor pain control after spine surgery was independently associated with failure to achieve the MCID (OR 2.35 [95% CI=1.59-3.46], p<.001) after adjusting for age (p=.18), female sex (p=.57), any nicotine products (p=.041), ASA physical status >2 (p<.001), ≥3 motion segment surgery (p=.008), revision surgery (p=.001), follow-up time (p<.001), and thoracolumbar surgery compared to cervical surgery (p=.004). The CAPPS score was also found to be independently predictive of poor surgical outcome. CONCLUSION: Poor pain control in the first 24-hours after elective spine surgery was an independent risk factor for poor surgical outcome. Perioperative treatment strategies to improve postoperative pain control may lead to improved patient-reported surgical 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".