Social determinants of health and recurrence of atrial fibrillation after catheter ablation: a Danish nationwide cohort study
Bibliographic record
Abstract
AIMS: To examine the associations between three social determinants of health (SDOH) and recurrence of AF after ablation. METHODS AND RESULTS: We selected patients who underwent a first ablation after an incident hospital diagnosis of AF between 2005 and 2018 from the entire Danish population. Educational attainment, family income, and whether the patient was living alone were assessed at the time of ablation. We used cause-specific proportional hazard models to estimate hazard ratios (HR) with 95% confidence interval (CI) adjusted for age and sex. In secondary analyses, we adjusted for comorbidities, antiarrhythmic medication, and prior electrical cardioversion.We selected 9728 patients (mean age 61 years, 70% men), and 5881 patients had AF recurrence over an average of 1.37 years after ablation (recurrence rate 325.7 (95% CI 317.6-334.2) per 1000 person-years). Lower education (HR 1.09 [1.02-1.17] and 1.07 [1.01-1.14] for lower and medium vs. higher), lower income [HR 1.14 (1.06-1.22) and 1.09 (1.03-1.17) for lower and medium vs. higher], and living alone [HR 1.07 (1.00-1.13)] were associated with increased rates of recurrence of AF. We found no evidence of interaction between sex or prior HF with SDOH. The association between family income and AF recurrence was stronger among patients < 65 years compared with those aged ≥ 65 years. The associations between SDOH and AF recurrence did not persist in the multivariable model. CONCLUSION: AF was more likely to recur among patients with lower educational attainment, lower family income, or those living alone. Multidisciplinary efforts are needed to reduce socioeconomic inequity in the effect of ablation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".