Association of care specialty with anticoagulant prescription and clinical outcomes in newly diagnosed atrial fibrillation: Results from the GARFIELD-AF registry
Bibliographic record
Abstract
OBJECTIVE: To determine whether stroke prevention strategy, comorbidity management, and clinical outcome risks differ across atrial fibrillation (AF) care specialties. METHODS: Newly diagnosed non-valvular AF patients enrolled in the international, prospective GARFIELD-AF registry (enrolment: 2010-2016) were analysed. Prescription of oral anticoagulation (OAC) therapy and select comorbidities was assessed by baseline care specialty: cardiology, primary care, or other specialties (internist/neurologist/geriatrician). Associations between care specialty and 2-year clinical outcomes were evaluated using multivariable Cox frailty models to account for within-country homogeneity. RESULTS: -VASc ≥2 patients was more common in cardiology care (31.0 %) than primary care (19.8 %) and other specialty care (24.9 %), but comorbidity management was similar across specialties. Compared to cardiology care, primary care was associated with greater non-cardiovascular mortality (1.21 [1.01-1.45]), major bleeding (1.31 [1.05-1.62]), and new/worsening heart failure risk (2.09 [1.69-2.59]). Care in other specialties was associated with greater all-cause (adjusted hazard ratio, 1.19 [95 % CI, 1.09-1.29]), cardiovascular (1.15 [1.01-1.31]), and non-cardiovascular mortality (1.29 [1.13-1.47]), as well as non-haemorrhagic stroke/systemic embolism (1.48 [1.26-1.73]), major bleeding (1.21 [1.02-1.43]), and new/worsening heart failure risk (1.45 [1.21-1.75]) than cardiology care. CONCLUSION: Comorbidity management was similar across AF care specialties, but patients outside of cardiology care had fewer NOAC prescriptions and greater risk for most clinical endpoints. Cardiology expertise may have important implications for AF prognosis. CLINICAL TRIAL REGISTRATION: URL: http://www. CLINICALTRIALS: gov. Unique identifier for GARFIELD-AF: NCT01090362.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".