Incident frailty and cognitive impairment by heart failure status in older patients with atrial fibrillation: the SAGE-AF study.
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) and heart failure (HF) frequently co-occur in older individuals. Among patients with AF, HF increases risks for stroke and death, but the associations between HF and incident cognition and physical impairment remain unknown. We aimed to examine the cross-sectional and prospective associations between HF, cognition, and frailty among older patients with AF. METHODS: The SAGE-AF (Systematic Assessment of Geriatric Elements in AF) study enrolled 1244 patients with AF (mean age 76 years, 48% women) from five practices in Massachusetts and Georgia. HF at baseline was identified from electronic health records using ICD-9/10 codes. At baseline and 1-year, frailty was assessed by Cardiovascular Health Survey score and cognition was assessed by the Montreal Cognitive Assessment. RESULTS: absent) was associated with both prevalent frailty (adjusted odds ratio [aOR]: 2.38, 95% confidence interval [CI]: 1.64-3.46) and incident frailty at 1 year (aOR: 2.48, 95% CI: 1.37-4.51). HF was also independently associated with baseline cognitive impairment (aOR: 1.60, 95% CI: 1.22-2.11), but not with developing cognitive impairment at 1 year (aOR 1.04, 95%CI: 0.64-1.70). CONCLUSIONS: Among ambulatory older patients with AF, the co-existence of HF identifies individuals with physical and cognitive impairments who are at higher short-term risk for becoming frail. Preventive strategies to this vulnerable subgroup merit consideration.
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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 | 0.000 | 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".