Comparison of the Impact of the Atrial Fibrillation Follow-Up Investigation of Rhythm Management Trial on Prescribing Patterns: A Time-Series Analysis
Bibliographic record
Abstract
BACKGROUND: The AFFIRM (Atrial Fibrillation Follow-Up Investigation of Rhythm Management) trial demonstrated that rate control and rhythm control strategies result in similar survival and quality of life for patients with atrial fibrillation (AF). Because of superior safety and lower cost, rate control is now the recommended strategy for the management of most elderly, high-risk AF patients. OBJECTIVE: To determine the extent to which the AFFIRM trial results have been adopted into actual practice. METHODS: We conducted a time-series analysis of 3 population-based cohorts of patients with AF who were 66 years of age or older in Pennsylvania and Ontario. We stratified patients in Ontario by socioeconomic status (SES) and examined changes in quarterly prescription rates for rate control and rhythm controlling medications as well as cardioversion procedures before and after publication of the AFFIRM trial. RESULTS: The publication of the AFFIRM trial resulted in statistically significant reductions in the use of rhythm controlling medications in all 3 cohorts (p < 0.01). The magnitude of these changes in the non-low SES Canadian cohort was approximately 1% per quarter and was greater than the magnitude observed in the other cohorts (p < 0.001). The use of cardioversion procedures also decreased in all study regions (p < 0.01). In contrast, AFFIRM publication was also associated with a small increase in the use of rate controlling medications in Canada (p < 0.01) but not in the US (p = 0.23). CONCLUSIONS: Publication of the AFFIRM trial resulted in small but statistically significant changes in the care of patients with AF.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".