Frailty Trajectories After Treatment for Coronary Artery Disease in Older Patients
Bibliographic record
Abstract
BACKGROUND: Frailty is an independent risk factor for cardiovascular outcomes. However, its trajectory after coronary artery disease treatment is unknown. METHODS AND RESULTS: Three hundred seventy-four patients undergoing nonemergent cardiac catheterization followed by treatment (ie, 128 coronary artery bypass graft [CABG], 150 percutaneous coronary intervention [PCI], 96 medical therapy only) were observed for 30 months. A frailty index (FI) score was calculated at baseline (before initial treatment) and 6, 12, and 30 months after treatment. Random-effects models compared FI score trajectories by sex, age, and treatment group. Mean baseline FI scores were 0.170, 0.154, and 0.154 for CABG, PCI, and medical therapy only, respectively. FI scores decreased (improved) 6 months after initial treatment, then increased (worsened) at 12 and 30 months (P<0.001 for differences over time). Women had nonsignificantly higher FI scores than men (P=0.097) but followed the same trajectory (P=0.352 for differences over time). In patients aged ≥75 years, FI scores increased postbaseline for CABG and medical therapy only and after 6 months for PCI patients. Patients <75 years assigned to PCI and CABG experienced a sustained frailty reduction, whereas those assigned to medical therapy only showed stable frailty over the 30-month follow-up period (P value for differences over time by age and treatment group=0.041). CONCLUSIONS: With coronary artery disease treatment, frailty generally follows a U-shaped trajectory, but the pattern may differ by age and treatment. Further investigation is needed to confirm these observations and determine whether patients might benefit from consideration of frailty status.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".