Frail Patients Are at Increased Risk for Mortality and Prolonged Institutional Care After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Frailty is an emerging concept in medicine yet to be explored as a risk factor in cardiac surgery. Where elderly patients are increasingly referred for cardiac surgery, the prevalence of a frail group among these is also on the rise. We assessed frailty as a risk factor for adverse outcomes after cardiac surgery. METHODS AND RESULTS: Functional measures of frailty and clinical data were collected prospectively for all cardiac surgery patients at a single center. Frailty was defined as any impairment in activities of daily living (Katz index), ambulation, or a documented history of dementia. Of 3826 patients, 157 (4.1%) were frail. Frail patients were older, were more likely to be female, and had risk factors for adverse surgical outcomes. By logistic regression, frailty was an independent predictor of in-hospital mortality (odds ratio 1.8, 95% CI 1.1 to 3.0), as well as institutional discharge (odds ratio 6.3, 95% CI 4.2 to 9.4). Frailty was an independent predictor of reduced midterm survival (hazard ratio 1.5, 95% CI 1.1 to 2.2). CONCLUSIONS: Frailty is a risk for postoperative complications and an independent predictor of in-hospital mortality, institutional discharge, and reduced midterm survival. Frailty screening improves risk assessment in cardiac surgery patients and may identify a subgroup of patients who may benefit from innovative processes of care.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".