Frailty efficacy as a predictor of clinical and cognitive complications in patients undergoing coronary artery bypass grafting: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Frailty is proposed as a predictor of outcomes in patients undergoing major surgeries, although data on the association of frailty and coronary artery bypass grafting (CABG) are lacking. We assessed the association between frailty and cognitive and clinical complications following CABG. METHODS: This prospective study included patients aged over 60 years undergoing elective CABG at Tehran Heart Center from 2020 to 2022. Baseline and three-month follow-up data on frailty using the Frail scale and clinical Frail scale, functional status using the Lawton Instrumental Activities of Daily Living Scale (IADL), cognitive function by Montreal Cognitive Assessment (MoCA), and depression by the Geriatric Depression Scale (GDS) were obtained. The incidence of adverse outcomes was investigated at the three-month follow-up. Outcomes between frail and non-frail groups were compared utilizing T-tests and Mann-Whitney U tests, as appropriate. RESULTS: We included 170 patients with a median age of 66 ± 4 years (75.3% male). Of these, 58 cases were classified as frail, and 112 individuals were non-frail, preoperatively. Frail patients demonstrated significantly worse baseline MOCA scores (21.08 vs. 22.41, P = 0.045), GDS (2.00 vs. 1.00, P = 0.009), and Lawton IADL (8.00 vs. 6.00, P < 0.001) compared to non-frail. According to 3-month follow-up data, postoperative MOCA and GDS scores were comparable between the two groups, while Lawton IADL (8.00 vs. 6.00, P < 0.001) was significantly lower in frail cases. A significantly higher rate of readmission (1.8% vs. 12.1%), sepsis (7.1% vs. 19.0%), as well as a higher Euroscore (1.5 vs. 1.9), was observed in the frail group. A mildly significantly more extended ICU stay (6.00 vs. 5.00, p = 0.051) was shown in the frail patient. CONCLUSION: Frailty showed a significant association with a worse preoperative independence level, cognitive function, and depression status, as well as increased postoperative complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".