Predicting poor postoperative pain control after elective spine surgery
Bibliographic record
Abstract
Background: Inadequate postoperative pain control after spine surgery is common and can lead to patient dissatisfaction and poor outcomes. Predictors for poorly controlled pain after spine surgery are unknown and preoperative prognostic tools are not available to aid in the identification of high-risk patients to help facilitate the development of personalized treatments. In this thesis, we performed (1) a systematic review on the predictors associated with poor pain control in surgical patients; (2) performed a retrospective cohort study evaluating predictors of poor postoperative pain control following spine surgery; and (3) developed and validated a clinical prediction score to identify patients at high-risk for developing poor pain control. Methods: (1) A random-effects model was used to meta-analyze the predictors for poor pain control after surgery in the systematic review. (2) Adults from the Canadian Spine Outcomes and Research Network registry who underwent elective cervical or thoracolumbar surgery were included. Preoperative predictors for poor pain control (mean numeric rating scale for pain>4 at rest during the first 24 hours after surgery) were identified using a multivariable logistic regression model. (3) The prediction score was developed and internally validated using a 70:30 split-sample method. Results: (1) Thirty-three studies representing 53,362 patients were included in the systematic review. Nine significant predictors for poor postoperative pain control were identified across surgical disciplines. (2) The retrospective cohort study included 1,300 patients, of which 56.7% had poor pain control after surgery. The multivariable model identified that younger age, female sex, preoperative daily opioid use, higher preoperative neck/back pain, higher depression scores on patient health questionnaire-9, ≥3 motion segment surgery, and fusion surgery were associated with poor pain control. (3) Patients identified as low-, high-, and extreme-risk by the score had 32.0%, 63.0%, and 85.0% probability of developing poor pain control, respectively. Conclusion: Seven significant predictors for poorly controlled pain after spine surgery were identified and incorporated into a prediction score. The score can discriminate patients at higher risk for, and accurately predict the probability of, developing poor pain control after surgery.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".