The association between atrial fibrillation and cognitive function in patients with heart failure
Bibliographic record
Abstract
Atrial fibrillation (AF) is associated with cognitive impairment in heart failure (HF). The purpose of this study was to examine whether AF independently predicted cognitive function in HF patients after controlling for more demographic, medical and psychological characteristics, and whether the timing of AF onset in relation to HF diagnosis independently contributed to cognitive function in HF patients with AF. A total of 188 hospitalized HF patients (62.8% male, age 66.3±10.6 years) completed cognitive function assessment with the Montreal Cognitive Assessment (MoCA). A history of AF, along with other medical characteristics, was ascertained through a review of participants’ medical charts. The timing of AF onset in relation to HF diagnosis was categorized into AF occurring prior to HF diagnosis (i.e. prior AF) and AF developing after HF diagnosis (i.e. incident AF). Altogether 72 participants had a positive diagnostic history of AF. Specifically, 41 had prior AF, and 31 developed AF subsequently. In HF patients, AF was associated with poorer performance on cognitive function after controlling for more confounders (β=−0.112, ΔR2=0.010, p=0.046). Among HF patients with AF, incident AF independently predicted poorer cognitive function (β=−0.238, ΔR2=0.027, p=0.047). AF independently contributes to cognitive function in HF patients after adjusting for more confounding variables. The timing of AF onset in relation to HF diagnosis independently predicts cognitive function in HF patients with AF. Prospective studies are needed to elucidate possible mechanisms for the association between AF and cognitive function in HF populations.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".