Hearing loss and cognitive decline among older adults with atrial fibrillation: the SAGE-AF study.
Bibliographic record
Abstract
OBJECTIVE: To examine the association between hearing loss and cognitive function cross-sectionally and prospectively among older adults with atrial fibrillation (AF). METHODS: = 1244) in the SAGE (Systematic Assessment of Geriatric Elements)-AF study were recruited from five internal medicine or cardiology clinics in Massachusetts and Georgia. Hearing was assessed by a structured questionnaire at baseline. Cognitive function was assessed by Montreal Cognitive Assessment (MoCA) at baseline and one year. Cognitive impairment was defined as score ≤ 23 on the MoCA. The associations between hearing loss and cognitive function were examined by multivariable adjusted logistic regression. RESULTS: = 451, 36%) were older, more likely to be male, and have depressive symptoms than patients without hearing loss. At baseline, 528 (42%) participants were cognitively impaired. Individuals with hearing loss were significantly more likely to have cognitive impairment at baseline [adjusted odds ratio (OR) = 1.37, 95% confidence interval (CI): 1.05-1.81]. Among the 662 participants who did not have cognitive impairment at baseline and attended the one-year follow-up visit, 106 (16%) developed incident cognitive impairment. Individuals with, versus those without, hearing loss were significantly more likely to develop incident cognitive impairment at one year (adjusted OR = 1.68, 95% CI: 1.07-2.64). CONCLUSIONS: Hearing loss is a prevalent but under-recognized factor associated with cognitive impairment in patients with AF. Assessment for hearing loss may be indicated among these patients to identify individuals at high-risk for adverse outcomes.
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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) | 0.000 | 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 | 0.000 |
| 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".