Centre-Specific Variation in Atrial Fibrillation Ablation-Treatment Rates in a Universal Single-Payer Healthcare System
Bibliographic record
Abstract
Background: Disparities in atrial fibrillation ablation rates have been studied previously, with a focus on either patient characteristics or systems factors, rather than geographic factors. The impact of electrophysiology (EP) centre practice patterns on ablation rates has not been well studied. Methods: This population-based cohort study used linked administrative datasets covering physician billing codes, hospitalizations, prescriptions, and census data. The study population consisted of patients who visited an emergency department with a new diagnosis of atrial fibrillation, in the period 2007-2016, in Ontario, Canada. Patient characteristics, including age, sex, medical history, comorbidities, socioeconomic factors, closest EP centre within 20 km, and distance to the nearest centre, were used as predictors in multivariable logistic regression models to assess the relationship between living in a location around specific EP centres and ablation rates. Results: ) score, lived closer to EP centres, and had fewer comorbidities than those who did not receive ablation treatment. Wide variation occurred in ablation rates, with adjacent census divisions having ablation rates up to 2.6 times higher. Multivariate regression revealed significant differences in ablation rates for patients who lived in a location around certain EP centres. The odds ratios for living in a location closest to specific centres ranged from 0.78 (95% confidence interval: 0.68-0.89) to 1.60 (95% confidence interval:1.34-1.90). Conclusions: Living near specific EP centres may significantly affect a patient's likelihood of receiving ablation treatment, regardless of factors such as age, gender, socioeconomic status, prior medical history, and distance to EP centres.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".