Can the ACS‐NSQIP surgical risk calculator predict postoperative complications in patients undergoing sacral tumor resection for chordoma?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The ACS-NSQIP surgical risk calculator is an online tool that estimates the risk of postoperative complications. Sacrectomies for chordoma are associated with a high rate of complications. This study was to determine if the ACS-NSQIP calculator can predict postoperative complications following sacrectomy. METHODS: Sixty-five (42 male, 23 female) patients who underwent sacrectomy were analyzed using the Current Procedural Terminology (CPT) codes: 49215 (excision of presacral/sacral tumor), 63001 (laminectomy of sacral vertebrae), 63728 (laminectomy for biopsy/excision of sacral neoplasm) and 63307 (sacral vertebral corpectomy for intraspinal lesion). The predicted rates of complications were compared to the observed rates. RESULTS: Complications were noted in 44 (68%) patients. Of the risk factors available to input to the ACS-NSQIP calculator, tobacco use (OR, 20.4; P < .001) was predictive of complications. The predicted risk of complications based off the CPT codes were: 49215 (16%); 63011 (6%); 63278 (11%) and 63307 (15%). Based on ROC curves, the use of the ACS-NSQIP score were poor predictors of complications (49215, AUC 0.65); (63011, AUC 0.66); (63307, AUC 0.67); (63278, AUC 0.64). CONCLUSION: The ACS-NSQIP calculator was a poor predictor of complications and was marginally better than a coin flip in its ability to predict complications following sacrectomy for chordoma.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".