Despite Having Worse Risk Profiles, Northern Albertans Wait Longer for Specialist Follow-up After Emergency Department Visits for Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation and flutter (AFF) are common arrhythmias diagnosed in the emergency department (ED), and prompt follow-up with specialists may yield better outcomes. This study examines time to first specialist outpatient visit following ED discharge for AFF. METHODS: Alberta residents aged ≥ 35 years with ED presentations for AFF ending in discharge during 2017-2018 were extracted and linked with hospitalizations and physician claims. A spatial scan and multinomial logistic regression were performed. Regression model predictors included demographics, prior diagnoses, and prior health service use. RESULTS: ED presentations for 4387 patients (54% male; mean age 68 years) were analyzed. Two geographic areas were identified as clusters that had longer times than would be expected by chance: a north cluster of northern areas with an estimated median time of 98 days (95% confidence interval [CI] 82,139), and an east cluster of eastern areas with a median of 57 days (95% CI 47, 68). Patients in the north cluster were more likely to be younger (adjusted odds ratio [aOR] = 0.76 per 5 years, 95% CI 0.62, 0.93) and have prior histories of AFF (aOR = 1.45, 95% CI 1.11, 1.90), congestive heart failure (aOR=1.51, 95% CI 1.15, 1.98), chronic obstructive pulmonary disease (aOR = 2.03, 95% CI 1.55, 2.65), and diabetes (aOR = 1.30, 95% CI 1.00, 1.67). They were less likely to have prior general practitioner outpatient visits (aOR = 0.65 per 5 visits, 95% CI 0.53, 0.81) and specialist outpatient visits (aOR = 0.39, 95% CI 0.30, 0.50) than other patients. CONCLUSIONS: Despite being at higher risk, patients in northern areas took longer to see a specialist after an ED presentation for AFF than those from other regions. Innovative strategies for promoting specialist follow-up should be explored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".