Impact of advanced age on management and prognosis in atrial fibrillation: insights from a population-based study in general practice
Bibliographic record
Abstract
OBJECTIVES: to examine the use of antithrombotic therapy and predictors of stroke and death in very elderly (≥85 years) atrial fibrillation (AF) patients in a general practice cohort from the UK. DESIGN: retrospective, observational cohort study; 12-month follow-up period. SETTING: eleven general practices serving the town of Darlington, England representing a population of 105,000 patients. PATIENTS: two thousand two hundred and fifty-nine patients with a history of AF, 561 (24.8%) aged ≥85 years. MAIN OUTCOME MEASURES: use of antithrombotic therapy by age group and predictors of stroke and death. RESULTS: five hundred and sixty-one (24.8%) AF patients aged ≥85 years (mean (SD) age 89 (4) years; 66% female) identified with a mean CHA2DS2-VASc score of 4.6 (SD 1.4). Thirty-six per cent received oral anticoagulation (OAC) compared with 57% in the 75-84 years age group. Forty-nine per cent of the very elderly received antiplatelet (AP) monotherapy; recorded OAC contraindications and declines were greatest among those aged ≥85 years. Stroke risk was highest among the very elderly (5.2% per annum), despite anticoagulation (3.9%). Multivariate analyses demonstrated an increased risk of stroke with AP monotherapy (odds ratio (OR) 2.45, 95% confidence intervals (CIs) 1.05-5.70) and a significant reduction in all-cause mortality with OAC therapy (OR 0.59, 95% CI 0.36-0.99). CONCLUSION: the majority of very elderly AF patients in general practice do not receive OAC despite their higher stroke risk; almost half received AP monotherapy. AP use independently increased the risk of stroke, signifying that effective stroke prevention requires OAC regardless of age, except where true contraindications exist.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".