Risk Prediction Accuracy Differs for Emergency Versus Elective Cases in the ACS-NSQIP
Bibliographic record
Abstract
BACKGROUND: Accurate risk estimation is essential when benchmarking surgical outcomes for reimbursement and engaging in shared decision-making. The greater complexity of emergency surgery patients may bias outcome comparisons between elective and emergency cases. OBJECTIVE: To test whether an established risk modelling tool, the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) predicts mortality comparably for emergency and elective cases. METHODS: From the ACS-NSQIP 2011-2012 patient user files, we selected core emergency surgical cases also common to elective scenarios (gastrointestinal, vascular, and hepato-biliary-pancreatic). After matching strategy for Common Procedure Terminology (CPT) and year, we compared the accuracy of ACS-NSQIP predicted mortality probabilities using the observed-to-expected ratio (O:E), c-statistic, and Brier score. RESULTS: In all, 56,942 emergency and 136,311 elective patients were identified as having a common CPT and year. Using a 1:1 matched sample of 37,154 emergency and elective patients, the O:E ratios generated by ACS-NSQIP models differ significantly between the emergency [O:E = 1.031; 95% confidence interval (CI) = 1.028-1.033] and elective populations (O:E = 0.79; 95% CI = 0.77-0.80, P < 0.0001) and the c-statistics differed significantly (emergency c-statistic = 0.927; 95% CI = 0.921-0.932 and elective c-statistic = 0.887; 95% CI = 0.861-0.912, P = 0.003). The Brier score, tested across a range of mortality rates, did not differ significantly for samples with mortality rates of 6.5% and 9% (eg, emergency Brier score = 0.058; 95% CI = 0.048-0.069 versus elective Brier score = 0.057; 95% CI = 0.044-0.07, P = 0.87, among 2217 patients with 6.5% mortality). When the mortality rate was low (1.7%), Brier scores differed significantly (emergency 0.034; 95% CI = 0.027-0.041 versus elective 0.016; 95% CI = 0.009-0.023, P value for difference 0.0005). CONCLUSION: ACS-NSQIP risk estimates used for benchmarking and shared decision-making appear to differ between emergency and elective populations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".