Frail Patients Are at Increased Risk for Mortality and Prolonged Institutional Care After Cardiac Surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 it