Abstract 4146563: Physician follow up and cardiac testing after a first diagnosis with secondary vs. primary atrial fibrillation in-hospital
Bibliographic record
Abstract
Background: Secondary atrial fibrillation (AF) is triggered by acute illness and associated with adverse outcomes. Timely follow-up is recommended by the American Heart Association statement on acute AF. Hypotheses: Patients with secondary AF receive less follow-up and cardiac testing than those primarily hospitalized for AF (primary AF). Follow-up is lower for secondary AF patients hospitalized for noncardiac diagnoses. Methods: Population-based cohort study using linked administrative datasets of patients aged ≥66 yrs discharged alive after a new diagnosis of AF while hospitalized in Ontario between Apr 2013 - Mar 2019. Patients were classified as secondary or primary AF using a validated approach based on discharge diagnosis type and followed for 1yr. Outcomes included physician visits (family physicians [FP], internists, cardiologists), and cardiac testing (electrocardiograms [ECG], echocardiograms, ambulatory ECG monitoring). The cumulative incidence function was used to quantify the incidence of outcomes. Cause-specific hazards regression was used to estimate hazard ratios (HR) associated with hospitalization type in secondary AF patients. Regression analyses accounted for competing risks. Results: We studied 13,011 secondary AF (35.2% cardiac surgery, 9.6% cardiac medical, 17% noncardiac surgery, 38.1% noncardiac medical) and 11,065 primary AF patients. Secondary AF was associated with lower age, male sex, less heart failure, and greater prevalence of other comorbidities. Less than 50% of secondary AF patients had visits to internists, cardiologists, echocardiograms or ambulatory ECG monitoring (see Figure). The incidence of all outcomes was significantly lower for secondary than primary AF. Among secondary AF patients, specialist follow-up and cardiac testing rates were lowest after noncardiac diagnoses (see Table). Conclusion: Patients with secondary AF have less specialist follow-up and cardiac testing than primary AF, especially if hospitalized for noncardiac diagnoses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".