Abstract 8925: Current Clinical AF Treatment Targets Do Not Reflect Patient Priorities
Bibliographic record
Abstract
Background: AF clinical trials define success as freedom from any AF episode >30 seconds. Clinically AF treatment is geared towards managing patient symptoms, yet little is known about patients’ expectations for successful AF treatment. Objective: Determine patient expectations for successful AF treatment. Methods: Patients with an AF diagnosis from a tertiary electrophysiology clinic were asked to complete the Toronto Atrial Fibrillation Severity Scale (AFSS) from the perspective of current symptoms, worst ever symptoms, and expected symptoms after successful treatment. Survey and patient clinical data were linked through the EMR. Results: Of the survey respondents (n=27), 41% were female, mean age was 69 (SD 9.7 years), and 30% had prior ablation. Overall, 59% of these patients considered AF treatment successful even with AF occurring one per year or more, and 70% would still consider treatment successful with episodes lasting minutes or more. When selecting the most important factor of success, 80% of patients selected decrease in AF frequency, 20% selected decrease in severity of symptoms, and 0 selected decrease in episode duration. A minority of patients (19%) considered treatment successful only if they never had AF again. Conclusion: A small minority of patients with AF would consider their treatment successful if they never experienced an AF episode again, which contradicts our current assessment of AF treatments. If confirmed in larger cohorts, these data support redesigning AF clinical studies to match outcomes prioritized by patients.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".