Frailty as an instrument for evaluation of elderly patients with non-ST-segment elevation myocardial infarction: A follow-up after more than 5 years
Bibliographic record
Abstract
BACKGROUND: There is a growing body of evidence on the relevance of using frailty measures also in a cardiovascular context. The estimated time to death is crucial in clinical decision-making in cardiology. However, data on the importance of frailty in long-term mortality are very scarce. The aim of the study was to assess the prognostic value of frailty on mortality at long-term follow-up of more than 5 years in patients 75 years or older hospitalised for non-ST-segment elevation myocardial infarction. We hypothesised that frailty is independently associated with long-term mortality. DESIGN: This was a prospective, observational study conducted at three centres. METHODS AND RESULTS: Frailty was assessed according to the Canadian Study of Health and Aging clinical frailty scale (CFS). Of 307 patients, 149 (48.5%) were considered frail according to the study instrument (degree 5-7 on the scale). The long-term all-cause mortality of more than 5 years (median 6.7 years) was significantly higher among frail patients (128, 85.9%) than non-frail patients (85, 53.8%), ( P < 0.001). In Cox regression analysis, frailty was independently associated with mortality from the index hospital admission to the end of follow-up (hazard ratio 2.06, 95% confidence interval 1.51-2.81; P < 0.001) together with age ( P < 0.001), ejection fraction ( P = 0.012) and Charlson comorbidity index ( P = 0.018). CONCLUSIONS: In elderly non-ST-segment elevation myocardial infarction patients, frailty was independently associated with all-cause mortality at long-term follow-up of more than 6 years. The combined use of frailty and comorbidity may be the ultimate risk prediction concept in the context of 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".