Clinical Features and Prognosis in Patients with Atrial Fibrillation and Prior Stroke: Comparing the Fushimi and Darlington AF Registries
Bibliographic record
Abstract
BACKGROUND: Ethnic differences in clinical characteristics, stroke risk profiles and outcomes among atrial fibrillation (AF) patients may exist. We therefore compared AF patients with previous stroke from Japan and the United Kingdom (UK). METHODS: We compared clinical characteristics, stroke risk and outcomes among AF patients from the Fushimi AF registry who had experienced a previous stroke (Japan; n=688; 19.7%) and the Darlington AF registry (UK; n=428; 19.0%). RESULTS: -VASc score was lower in AF patients in Fushimi than those in Darlington (5.18 vs. 5.57; p<0.01), oral anticoagulation (OAC) was prescribed significantly more frequently in Fushimi (68.3%) than Darlington (61.7%) (p=0.02). Multivariate logistic regression analysis showed that Japanese ethnicity was associated with a significantly decreased risk of recurrent stroke (OR 0.59. 95% CI 0.36-0.97; p=0.04) but a significantly increased risk of all-cause mortality (OR 1.76, 95% CI 1.18-2.66; p<0.01) in AF patients with previous stroke. CONCLUSIONS: AF patients with previous stroke in the UK were at higher risk of recurrent stroke compared to Japanese patients, but OAC was utilised less frequently. There was a lower risk of recurrent stroke in the secondary prevention cohort from the Fushimi registry, but an increased risk of all-cause mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".