Abstract 25: Accuracy of Patient Identification of Atrial Fibrillation in the Clinic Setting
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) related symptoms are commonly reported but often difficult to directly correlate with arrhythmia. Given that symptoms largely drive management, correct patient awareness of AF is critical to implementation of appropriate therapy. Objective: To determine accuracy of patient identification of their own atrial rhythm. Methods: All AF patients in our center undergo patient reported outcome assessment with the Toronto AF Severity Scale (AFSS) immediately prior to clinic visit. Using the validated questions from the AFSS, we assessed if patients were able to accurately determine their rhythm (AF or not) compared to an electrocardiogram (ECG) during the same clinic visit. Results: We identified 254 unique patients (Table 1) with interpretable ECG and AFSS data available from the same visit. Based on ECG, 81% (n=206) were not in AF. 20 of these 206 patients (9.7%) incorrectly identified themselves in AF (Figure 1). Patients who incorrectly thought they were in AF were significantly older (p<0.05). Of the 48 (18.9%) that were in AF by ECG, 15 (31.2%) incorrectly identified themselves in sinus rhythm. There was no significant difference between sex, age or mean heart rate in patients in AF. Overall 14% (35 of 254) provided an assessment that was inconsistent with their ECG. Conclusion: Approximately one of ten patients incorrectly identified themselves as being in AF despite being in sinus rhythm. Without a standardized method to confirm if symptoms truly correlate with AF these patients may be at risk for unnecessary antiarrhythmic therapies. On the contrary, nearly a third of patients were unaware of AF which reemphasizes the incidence of asymptomatic 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 | 0.010 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".