The Association of Frailty With Outcomes and Resource Use After Emergency General Surgery: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Older patients undergoing emergency general surgery (EGS) experience high rates of postoperative morbidity and mortality. Studies focused primarily on elective surgery indicate that frailty is an important predictor of adverse outcomes in older surgical patients. The population-level effect of frailty on EGS is poorly described. Therefore, our objective was to measure the association of preoperative frailty with outcomes in a population of older patients undergoing EGS. METHODS: We created a population-based cohort study using linked administrative data in Ontario, Canada, that included community-dwelling individuals aged >65 years having EGS. Our main exposure was preoperative frailty, as defined by the Johns Hopkins Adjusted Clinical Groups frailty-defining diagnoses indicator. The Adjusted Clinical Groups frailty-defining diagnoses indicator is a binary variable that uses 12 clusters of frailty-defining diagnoses. Our main outcome measures were 1-year all-cause mortality (primary), intensive care unit admission, length of stay, institutional discharge, and costs of care (secondary). RESULTS: Of 77,184 patients, 19,779 (25.6%) were frail. Death within 1 year occurred in 6626 (33.5%) frail patients compared with 11,366 (19.8%) nonfrail patients. After adjustment for sociodemographic and surgical confounders, this resulted in a hazard ratio of 1.29 (95% confidence interval [CI] 1.25-1.33). The risk of death for frail patients varied significantly across the postoperative period and was particularly high immediately after surgery (hazard ratio on postoperative day 1 = 23.1, 95% CI 22.3-24.1). Frailty was adversely associated with all secondary outcomes, including a 5.82-fold increase in the adjusted odds of institutional discharge (95% CI 5.53-6.12). CONCLUSIONS: After EGS, frailty is associated with increased rates of mortality, institutional discharge, and resource use. Strategies that might improve perioperative outcomes in frail EGS patients need to be developed and tested.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".