Frailty in Patients With Acute Coronary Syndrome: Comparisons Among Three Frailty Screening Tools in Predicting In-Hospital Adverse Events
Bibliographic record
Abstract
Objectives: The aims of the study were to investigate the proportion of frailty among inpatients having Acute Coronary Syndrome (ACS) and to compare the prognostic value of three frailty screening methods in predicting in-hospital adverse events among the study population. Methods: This prospective, observational study design was conducted on older patients with ACS. Data was collected using a structured questionnaire on general characteristics (age, gender, comorbidities, body mass index) and medical records (admission diagnosis, ACS type, angiography results, treatment therapy, left ventrical ejection fraction, and length of hospital stay). Frailty was assessed using three frailty screening scales: Reported Edmonton Frail Scale (REFS), Clinical Frailty Scale (CFS), and Frail Scale (FS). Results: A total of 116 older patients, the mean age was 72.9 (SD: 6.2) years. Prevalence of frailty in older inpatients with ACS was 44.8%, 35.3%, and 32.7% according to REFS; CFS, and FS, respectively. In addition, 75.9% were treated with percutaneous coronary intervention and the length of hospitalization was 6.3 (SD: 3.8) days. The AUC in the prognosis of net adverse clinical events (NACE) for patients with ACS was 0.74 (with REFS ≥ 7 points), 0.76 (with CFS ≥ 5 points), and 0.80 (with FS ≥ 2 points). Kappa values of 0.49, 0.57, and 0.47 were observed for REFS, CFS, and FS. Conclusion: This study compared three frailty screening tools in predicting in-hospital adverse events among Acute Coronary Syndrome. The Frail Scale showed the highest value to predict NACE and demonstrated its superiority over other frailty scales.
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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 | 0.012 |
| 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".