Detecting Patients With Nonvalvular Atrial Fibrillation and Atrial Flutter in the Canadian Primary Care Sentinel Surveillance Network: First Steps
Bibliographic record
Abstract
BACKGROUND: A recent feasibility assessment of quality indicators for nonvalvular atrial fibrillation/atrial flutter (NVAF/AFL) identified the Canadian Primary Care Sentinel Surveillance Network, a national outpatient electronic medical record (EMR) system, as a data source for measurement. As a first step, we adapted and validated an existing EMR case definition. METHODS: A diagnosis of NVAF/AFL was defined using International Classification of Disease, 9th Revision, Clinical Modification codes (427.3) in either the physician billing, encounter diagnosis, or health condition fields. We identified all presumed cases in a single clinical site with the algorithm and selected a random sample of those who were presumed NVAF/AFL negative with the same algorithm. A chart audit diagnosis of "definite" NVAF/AFL was confirmed by electrocardiogram and nonvalvular diagnosis confirmed after echocardiogram, attending physician, or specialist letter review. To demonstrate face validity, clinical characteristics were compared for patients with and without NVAF/AFL. RESULTS: The case definition identified a possible 184 patients with and 184 without NVAF/AFL. The case validation resulted in a sensitivity of 100% (95% confidence interval [CI], 100-100), specificity of 84.3% (95% CI, 78.8-89.9), and positive and negative predictive value of 74.7% (95% CI, 66.4-83.2) and 100% (95% CI 100-100), respectively. Patients with NVAF/AFL were older (63 vs 42 years) and had a higher proportion of cardiovascular comorbidities and relevant medications. CONCLUSIONS: We think it is possible that with further validation work, NVAF/AFL can be accurately identified using this large pan-Canadian EMR system and used as a future tool to measure quality of care in the outpatient setting.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".