Modified Frailty Index for Patients Undergoing Surgery for Colorectal Cancer: Analysis of the National Inpatient Sample From 2015 to 2019
Why this work is in the frame
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Bibliographic record
Abstract
Background Frailty is increasingly recognized as a perioperative risk for numerous surgical diseases. We applied the modified frailty index (mFI-11) to the National Inpatient Sample (NIS) for patients undergoing surgery for colorectal cancer (CRC). Methods We performed a retrospective analysis of the NIS (2015-2019) including CRC patients undergoing surgery. We classified patients into frail (ie, mFI ≥0.27) and robust (ie, mFI <0.27) categories. Primary outcomes were in-hospital postoperative morbidity and mortality. The secondary outcomes included system-specific postoperative morbidity and length of stay (LOS). Multivariable regression models were fit. Results Within the 53,652 identified patients undergoing surgery for CRC, 19.1% were frail. Frail patients were at higher risk of postoperative mortality (3.1% vs 1.0%, odds ratio [OR] 1.96, 95% confidence intervals [CIs] 1.68-2.30, P < 0.001), morbidity (41.3 % vs 23.1%, OR 1.75, 95% CI 1.66-1.83, P < 0.001), and LOS (mean difference [MD] 1.46, 95% CI 0.29-1.62, P < 0.001). Significant differences existed between groups in system-specific postoperative morbidity, with the largest effect estimates seen in cardiovascular morbidities (OR 4.07, 95% CI 3.36-4.93, P = 0.001), followed by respiratory (OR 1.75, 95% CI 1.66-1.83, P = 0.001). Conclusion Frail patients undergoing CRC surgery are at risk of increased postoperative complications. Preoperative frailty screening may allow for individualized preoperative counseling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it