P576Is atrial or ventricular dysfunction a contributor to cognitive impairment in heart failure patients?
Bibliographic record
Abstract
Background: Cognitive impairment is highly prevalent among heart failure (HF) patients and has recently been recognised as an important predictor of short-term adverse outcomes in this population. Purpose: We sought whether cognitive function in HF is associated with atrial or ventricular dysfunction. Methods: 731 HF pts (61% male, median age 75 years, and 55% with EF<40%) were recruited in five States of Australia (New South Wales, Queensland, South Australia, Tasmania and Victoria). Cardiac function was assessed by 2D echocardiography. Cognitive function (including seven cognitive domains) was assessed using the Montreal Cognitive Assessment (MoCA). Clinical, socio-demographic and blood biochemical factors were also collected. Results: Among 731 HF patients, 28% had mild cognitive impairment (MoCA score 17–23) and 16% had dementia (MoCA score<17). Echo parameters of diastolic function, but not systolic function, were correlated with worse MoCA score. Among these parameters, left atrial volume index (LAVi) was the strongest predictor (r=−0.23 p<0.001) of cognitive function, and was most strongly associated with memory (r=−0.21 p<0.001) and orientation (r=−0.23 p<0.001). This association was independent of age, sex, Charlson comorbidity index, HF classification, brain natriuretic peptide and socio-demographic factors. Stratified analysis showed a stronger association of LAVi with cognitive function among patients without atrial fibrillation (r=−0.27 p<0.001) than those with atrial fibrillation (r=−0.16 p=0.009).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".