Comparison of frailty instruments in acute coronary syndrome patients
Bibliographic record
Abstract
Abstract Background Recognising frailty is important to guide clinical decisions in older patients with cardiac disease. The relative strengths of different frailty instruments to predict mortality and hospitalization risk are uncertain. Methods The Edmonton Frail Scale (EFS), Fried Criteria (Fried), Clinical Frailty Scale (CFS), Katz score (Katz), GP Cognition test (GPCog), and Euroscore II a disease based risk score, were completed in 1174 clinically stable inpatients >70 years of age admitted with an acute coronary syndrome to 5 New Zealand hospitals. Associations with all cause mortality (n=353, 29%) during a median follow-up of 5.1 (IQR: 4.6-5.5) years and hospitalization for > 10 days in the next year (n=267, 22%) were evaluated. Results There were graded associations between increasing frailty assessed by each tool and all cause mortality. For the EFS, which scores up to 17 points on different dimensions of frailty, hazard ratios for high (score 9-17, n=197) compared to low frailty (score 0-2, n=331) were 5.0 (95%CI: 3.4-7.4) for mortality, and 5.3 (3.4-8.3) for hospitalization. Discrimination for all-cause mortality according to Harrell’s C-index for each instrument were EFS 0.663, Euroscore II 0.654, Fried 0.648, CFS 0.640, GPCog 0.608, and Katz 0.593, P<0.001 for all. C-statistics for hospitalization >10 days were EFS 0.649, Fried 0.628, Katz 0.602, Euroscore II 0.589, CFS 0.584, and GPCog 0.552, P<0.001 for all. When combining tools integrated discrimination improvement for both mortality and hospitalization were greater for EFS than for other frailty instruments. Conclusion In acute coronary syndrome patients aged >70 years greater ‘frailty’ assessed using all tools was associated with higher mortality and hospitalization. The Edmonton Frail Scale, which provides a graded measure of severity of frailty based on information relevant to clinical care, discriminated the risk of mortality and hospitalization as well or better than other frailty instruments.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".