Development and Validation of a Clinical Prediction Score for Poor Postoperative Pain Control Following Elective Spine Surgery
Bibliographic record
Abstract
INTRODUCTION: Approximately, 30% to 64% of people suffer from poorly controlled pain following spine surgery leading to patient dissatisfaction and poor outcomes. The ability to identify these patients before surgery may be useful to facilitate patient education and the development of personalized clinical-care pathways to improve postoperative pain management. METHODS: Adult patients were consecutively enrolled through the Canadian Spine Outcomes and Research Network registry and were included if they underwent inpatient elective cervical or thoracolumbar spine surgery. The outcome was poor postoperative pain control defined as the mean numeric rating scale for pain >4 in the first 24-h after surgery. A split-sample design was used to develop and validate the prediction model. The prediction model was transformed into a risk-based score and simplified to a 3-tier Calgary Postoperative Pain after Spine Surgery (CAPPS) score to maximize clinical utility. RESULTS: Of 1300 patients, 57% had poorly controlled pain following spine surgery. Seven significant predictors were associated with poor pain control: younger age, female sex, preoperative daily opioid medication use, higher preoperative neck or back pain intensity, higher PHQ-9 depression score, > = 3 motion segment operation, and fusion surgery. Notably, chronic pain and minimally invasive surgery were not associated with pain control status. The model was discriminative (c-statistics 0.74 [95% CI = 0.71-0.77]) and accurate (Hosmer-Lemeshow goodness-of-fit, P = .99) at predicting the outcome. Patients classified to low-, high-, and extreme-risk groups by the CAPPS score had 32%, 63%, and 85% predicted probability of developing poor postoperative pain control, respectively. This closely mirrored the observed probability of 37%, 62%, and 81% in the same risk-groups for poor pain control in the validation cohort. CONCLUSION: This internally validated CAPPS score based on 7 easily acquired characteristics accurately predicted the probability of developing poor pain control after spine surgery. This score can be used to develop personalized preoperative and perioperative treatment strategies to improve pain 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".