The Essential Frailty Toolset in Older Adults Undergoing Coronary Artery Bypass Surgery
Bibliographic record
Abstract
Background The Essential Frailty Toolset (EFT) was shown to be easy to use and predictive of adverse events in patients undergoing aortic valve replacement procedures. The objective of this study was to evaluate the EFT in patients undergoing coronary artery bypass grafting procedures. Methods and Results The McGill Frailty Registry prospectively included patients ≥60 years of age undergoing urgent or elective isolated coronary artery bypass grafting between 2011 and 2018 at 2 hospitals. The preoperative EFT was scored 0 to 5 points as a function of timed chair rises, Mini‐Mental Status Examination, serum albumin, and hemoglobin. The primary outcome was all‐cause mortality assessed by Cox proportional hazards regression. The cohort consisted of 500 patients with a mean age of 71.4 ± 6.4 years, of which 27% presented with acute coronary syndromes requiring urgent surgery. The mean EFT was 1.3 ± 1.1 points, 132 (26%) were nonfrail, 298 (60%) were prefrail, and 70 (14%) were frail. Over a median follow‐up of 4.0 years, 78 deaths were observed. In nonfrail, prefrail, and frail patients, survival at 1 year was 98%, 95%, and 91%, and at 5 years was 89%, 83%, and 63% ( P <0.001). After adjustment, each incremental EFT point was associated with a hazard ratio of 1.28 (95% CI, 1.05–1.56) and frail patients had a 3‐fold increase in all‐cause mortality. Conclusions The EFT is a pragmatic and highly prognostic tool to assess frailty and guide decisions for coronary artery bypass grafting in older adults. Furthermore, the EFT may be actionable through targeted interventions such as cardiac rehabilitation and nutritional optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".