Characteristics associated with poor atrial fibrillation-related quality of life in adults with atrial fibrillation
Bibliographic record
Abstract
PURPOSE: Few studies have examined the relationship between poor atrial fibrillation-related quality of life (AFQoL) and a battery of geriatric factors. The objective of this study is to describe factors associated with poor AFQoL in older adults with atrial fibrillation (AF) with a focus on sociodemographic and clinical factors and a battery of geriatric factors. METHODS: Cross-sectional analysis of a prospective cohort study of participants aged 65+ with high stroke risk and AF. AFQoL was measured using the validated Atrial Fibrillation Effect on Quality of Life (score 0-100) and categorized as poor (<80) or good (80-100). Chi-square and t -tests evaluated differences in factors across poor AFQoL and significant characteristics ( P < 0.05) were entered into a logistic regression model to identify variables related to poor AFQoL. RESULTS: Of 1244 participants (mean age 75.5), 42% reported poor AFQoL. Falls in the past 6 months, pre/frail and frailty, depression, anxiety, social isolation, vision impairment, oral anticoagulant therapy, rhythm control, chronic obstructive pulmonary disease and polypharmacy were associated with higher odds of poor AFQoL. Marriage and college education were associated with a lower odds of poor AFQoL. CONCLUSIONS: More than 4 out of 10 older adults with AF reported poor AFQoL. Geriatric factors associated with higher odds of reporting poor AFQoL include recent falls, frailty, depression, anxiety, social isolation and vision impairment. Findings from this study may help clinicians screen for patients with poor AFQoL who could benefit from tailored management to ensure the delivery of patient-centered care and improved well being among older adults with AF.
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 | 0.000 |
| 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 | 0.000 |
| 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".