Are the frail destined to fail? Frailty index as predictor of surgical morbidity and mortality in the elderly
Bibliographic record
Abstract
BACKGROUND: America's aging population has led to an increase in the number of elderly patients necessitating emergency general surgery. Previous studies have demonstrated that increased frailty is a predictor of outcomes in medicine and surgical patients. We hypothesized that use of a modification of the Canadian Study of Health and Aging Frailty Index would be a predictor of morbidity and mortality in patients older than 60 years undergoing emergency general surgery. METHODS: Data were obtained from the National Surgical Quality Improvement Program Participant Use Files database in compliance with the National Surgical Quality Improvement Program Data Use Agreement. We selected all emergency cases in patients older than 60 years performed by general surgeons from 2005 to 2009. The effect of increasing frailty on multiple outcomes including wound infection, wound occurrence, any infection, any occurrence, and mortality was then evaluated. RESULTS: Total sample size was 35,334 patients. As the modified frailty index increased, associated increases occurred in wound infection, wound occurrence, any infection, any occurrence, and mortality. Logistic regression of multiple variables demonstrated that the frailty index was associated with increased mortality with an odds ratio of 11.70 (p < 0.001). CONCLUSION: Frailty index is an important predictive variable in emergency general surgery patients older than 60 years. The modified frailty index can be used to evaluate risk of both morbidity and mortality in these patients. Frailty index will be a valuable preoperative risk assessment tool for the acute care surgeon. LEVEL OF EVIDENCE: Prognostic study, level II.
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".