Risk for Stroke After Newly Diagnosed Atrial Fibrillation During Hospitalization for Other Primary Diagnoses
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) that is first diagnosed during hospitalization for other causes can subside with resolution of the inciting stressor. OBJECTIVE: To describe the risk for stroke after newly diagnosed AF during hospitalization for other causes. DESIGN: Population-based retrospective cohort study. SETTING: Ontario, Canada. PARTICIPANTS: Patients aged 66 years or older discharged alive from the hospital between April 2013 and March 2023 with a first diagnosis of AF. INTERVENTION: Newly diagnosed AF during hospitalization for other causes, categorized into cardiac medical, noncardiac medical, cardiac surgical, and noncardiac surgical. MEASUREMENTS: The primary outcome was hospitalization for stroke. The cumulative incidence function was used to estimate crude incidence, censoring on anticoagulant dispensation. Inverse probability of censoring weights were used to account for informative censoring. RESULTS: -VA scores of 5 to 8. LIMITATION: Long-standing AF may have been misclassified as newly diagnosed, leading to overestimation of stroke risk. CONCLUSION: -VA scores greater than 4 approximated the 2% threshold commonly used to initiate anticoagulation in AF. PRIMARY FUNDING SOURCE: Canadian Cardiovascular Society.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".