Catheter ablation for the treatment of atrial fibrillation is associated with a reduction in health care resource utilization
Bibliographic record
Abstract
BACKGROUND: Catheter ablation (CA) is superior to antiarrhythmic therapy at reducing recurrence of atrial fibrillation (AF); however, there are limited data regarding whether this decrease translates into a reduction in health care resource utilization. OBJECTIVE: To evaluate the impact of AF ablation on long-term health care resource utilization. METHODS: A population-based cohort was constructed to include patients who underwent CA for AF in Quebec, Canada, between April 2005 and March 2011. Resource utilization was evaluated 24 months pre- and postindex CA procedure. RESULTS: In a cohort of 1,556 patients, resource utilization increased progressively over the 24-month period leading to index CA (P for trend <0.05 for hospitalizations, ER visits, outpatient visits, cardioversions, and echocardiograms). After index CA, all-cause hospitalizations, hospitalizations for AF, ER visits, cardioversions, and echocardiograms were reduced 12 months post-CA compared to 12 months prior (all-cause hospitalizations 0.8-0.6 per patient per year; hospitalizations for AF 0.4-0.3; ER visits 2.9-1.8; cardioversions 0.5-0.2; echocardiograms 0.8-0.5; P < 0.05 for all trends). Resource utilization continued to decline at 24 months post-CA (vs. 12 months prior) for all-cause hospitalizations (0.4), cardioversions (0.1), and echocardiograms (0.3) (per patient year; P < 0.05 for all trends). CONCLUSION: In conclusion, the pattern of increasing health care resource utilization preceding CA for AF reverses after CA to lower than preablation levels up to 24 months post-CA.
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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 | 0.003 |
| 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 | 0.001 | 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".