Validation of the Calgary Postoperative Pain after Spine Surgery Score for Poor Postoperative Pain Control after Spine Surgery
Bibliographic record
Abstract
OBJECTIVE: The Calgary Postoperative Pain after Spine Surgery (CAPPS) score was developed to identify patients at risk of experiencing poorly controlled pain after spine surgery. The goal of this study was to independently validate the CAPPS score on a prospectively collected patient sample. METHODS: Poor postoperative pain control was defined as a mean numeric rating scale (NRS) for pain >4 at rest in the first 24 hours after surgery. Baseline characteristics in this study (validation cohort) were compared to those of the development cohort used to create the CAPPS score. Predictive performance of the CAPPS score was assessed by the area under the curve (AUC) and percentage misclassification for discrimination. A graphical comparison between predicted probability vs. observed incidence of poorly controlled pain was performed for calibration. RESULTS: Fifty-two percent of 201 patients experienced poorly controlled pain. The validation cohort exhibited lower depression scores and a higher proportion using daily opioid medications compared to the development cohort. The AUC was 0.74 [95%CI = 0.68-0.81] in the validation cohort compared to 0.73 [95%CI = 0.69-0.76] in the development cohort for the eight-tier CAPPS score. When stratified between the low- vs. extreme-risk and low- vs. high-risk groups, the percentage misclassification was 21.2% and 30.7% in the validation cohort, compared to 29.9% and 38.0% in the development cohort, respectively. The predicted probability closely mirrored the observed incidence of poor pain control across all scores. CONCLUSIONS: The CAPPS score, based on seven easily obtained and reliable prognostic variables, was validated using a prospectively collected, independent sample of patients.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".