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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".