Which frailty score in cardiac surgery patients?
Bibliographic record
Abstract
Background: Frailty assessment for risk prediction is suggested in elderly patients undergoing cardiac surgery. We aimed to compare five different frailty tests. Methods: Relation of Edmonton Frailty Score (EFS), Fried Frailty Phenotype (FFP), FRAIL (Fatigue, Resistance, Ambulation, Illness, and Loss of weight), Katz and hand grip strength (HGS) tests to each other, postoperative outcomes and mortality rates were evaluated prospectively in 140 consecutive patients aged ≥65 years. Results: The median follow-up period was 880.5 (range, 0 to 1,237) days with higher EFS and FFP scores in non-survivors (p<0.05). Patients with any complication had higher EFS (p=0.002), FFP (p=0.004) and FRAIL (p=0,006) scores. Compared to non-frail patients, frail patients' NYHA capacity, EuroSCORE II and STS mortality risks were higher; hemoglobin values and HGS were lower with EFS, FFP, and FRAIL tests. Frail patients' hospitalization periods with EFS (p=0.003) and intensive care unit stay with FFP (p=0.029) were longer. No mortality was observed in non-frail patients according to the FFP test. The Kaplan-Meier (KM) log-rank survival curves showed significant differences in favor of non-frail subgroups according to EFS, FFP and HGS tests (p<0.05). Relative risks for mortality in frail and pre-frail patients were between 0.9 and 4. The FFP was the most sensitive test (area under curve=0.721). There was discordance rather than concordance among five different tests (Kappa <0.411). Conclusion: For patients aged ≥65 years undergoing heart surgery the FFP can be used safely to determine non-frail patients. Although the EFS seems to be promising to identify frail patients, further large-scale studies using various tests are needed to predict an optimal cut-off value for this patient population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".