Frailty is independently associated with 1-year mortality for elderly patients with non-ST-segment elevation myocardial infarction
Bibliographic record
Abstract
BACKGROUND: For the large population of elderly patients with cardiovascular disease, it is crucial to identify clinically relevant measures of biological age and their contribution to risk. Frailty is denoting decreased physiological reserves and increased vulnerability. We analysed the manner in which the variable frailty is associated with 1-year outcomes for elderly non-ST-segment elevation myocardial infarction (NSTEMI) patients. METHODS AND RESULTS: Patients aged 75 years or older, with diagnosed NSTEMI were included at three centres, and clinical data including judgment of frailty were collected prospectively. Frailty was defined according to the Canadian Study of Health and Aging Clinical Frailty Scale. Of 307 patients, 149 (48.5%) were considered frail. By Cox regression analyses, frailty was found to be independently associated with 1-year mortality after adjusting for cardiovascular risk and comorbid conditions (hazard ratio 4.3, 95% CI 2.4-7.8). The time to the first event was significantly shorter for frail patients than for nonfrail (34 days, 95% CI 10-58, p = 0.005). CONCLUSIONS: Frailty is strongly and independently associated with 1-year mortality. The combined use of frailty and comorbidity may constitute an important risk prediction concept in regard to cardiovascular patients with complex needs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".