Association of psychosocial factors with <scp>all‐cause</scp> hospitalizations in patients with atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: A high burden of cardiovascular comorbidities puts patients with atrial fibrillation (AF) at high risk for hospitalizations, but the role of other factors is less clear. HYPOTHESIS: To determine the relationship between psychosocial factors and the risk of unplanned hospitalizations in AF patients. METHODS: Prospective observational cohort study of 2378 patients aged 65 or older with previously diagnosed AF across 14 centers in Switzerland. Marital status and education level were defined as social factors, depression and health perception were psychological components. The pre-defined outcome was unplanned all-cause hospitalization. RESULTS: During a median follow-up of 2.0 years, a total of 1713 hospitalizations occurred in 37% of patients. Compared to patients who were married, adjusted rate ratios (aRR) for all-cause hospitalizations were 1.28 (95% confidence interval [CI], 0.97-1.69) for singles, 1.31 (95%CI, 1.06-1.62) for divorced patients, and 1.02 (95%CI, 0.82-1.25) for widowed patients. The aRRs for all-cause hospitalizations across increasing quartiles of health perception were 1.0 (highest health perception), 1.15 (95%CI, 0.84-1.59), 1.25 (95%CI, 1.03-1.53), and 1.66 (95%CI, 1.34-2.07). No different hospitalization rates were observed in patients with a secondary or primary or less education as compared to patients with a college degree (aRR, 1.06; 95%CI, 0.91-1.23 and 1.05; 95%CI, 0.83-1.33, respectively). Presence of depression was not associated with higher hospitalization rates (aRR, 0.94; 95%CI, 0.68-1.29). CONCLUSIONS: The findings suggest that psychosocial factors, including marital status and health perception, are strongly associated with the occurrence of hospitalizations in AF patients. Targeted psychosocial support interventions may help to avoid unnecessary hospitalizations. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT02105844.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".