Frailty of patients scheduled for cardiac surgery — a pilot study
Bibliographic record
Abstract
Introduction. Frailty has been recently approved in many surgical fields as the acknowledged preoperative predictor of adverse postoperative complications. Several methods are available to assess frailty assessment which focus on different patient-related data. The aims of the study were: 1) to verify whether frailty may predict early postoperative complications in cardiac surgery; and 2) to investigate the agreement between objective and subjective assessment of frailty. Material and methods. This prospective study included 54 consecutive patients (32 men; median age 75 years) hospitalized between December 2015 and February 2016. Frailty was assessed using the Edmonton Frail Scale (EFS, subjective tool) and the Modified Frailty Index (MFI, objective tool). Complications were evaluated based on medical records. Results. The median EFS was 6 (IQR 5–7) points. Frailty was observed in 15% and vulnerability in 49% of subjects. The median MFI was 0.45 (IQR 0.36–0.56). We found a weak correlation between frailty and the length of hospital stay (EFS: r = 0.22; P = 0.1; MFI: r = 0.324; P = 0.02). Neither tools could predict the occurrence of postoperative complications (EFS: AUROC = 0.602; 95% CI 0.459–0.732; P = 0.2; MFI: AUROC = 0.532; 95% CI 0.389–0.670; P = 0.2). We found no correlation between EFS and MFI (r = 0.05, P = 0.7). Conclusions. Although many elderly cardiac surgical patients are at risk of frailty, none of the evaluated methods could predict postoperative complications. Available diagnostic tools to assess frailty cannot be used interchangeably. Subjective assessment (by a patient) should be verified by objective evaluation (by a treating physician) and conclusions should be drawn based on the overall clinical picture.
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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) | 0.001 | 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 | 0.001 | 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".