Risk stratification schemes, anticoagulation use and outcomes: the risk–treatment paradox in patients with newly diagnosed non-valvular atrial fibrillation
Bibliographic record
Abstract
OBJECTIVE: To examine whether warfarin use and outcomes differ across CHADS(2) and CHA(2)DS(2)-VASc risk strata for non-valvular atrial fibrillation (NVAF). DESIGN: Population-based cohort study using linked administrative databases in Alberta, Canada. SETTING: Inpatient and outpatient. PATIENTS: 42,834 consecutive patients ≥ 20 years of age with newly diagnosed NVAF. MAIN OUTCOME MEASURES: Cerebrovascular events and/or mortality in the first year after diagnosis. RESULTS: Of 42,834 NVAF patients, 22.7% were low risk on the CHADS(2) risk score (0), 27.5% were intermediate risk (1), and 49.8% were high risk (≥ 2). The CHA(2)DS(2)-VASc risk score reclassified 16,722 patients such that 7.8% were defined low risk, 13.8% intermediate risk and 78.4% high risk. Of the elderly cohort (≥ 65 years) with definite NVAF visits (at least two encounters 30 days apart, n = 8780), 49% were taking warfarin within 90 days of diagnosis. Warfarin use did not differ across risk strata using either the CHADS(2) (p for trend = 0.85) or CHA(2)DS(2)-VASC (p = 0.35). In multivariable adjusted analyses, warfarin use was associated with substantially lower rates of death or cerebrovascular events for patients with CHADS(2) scores of 1 (OR 0.52, 95% CI 0.41 to 0.67) or ≥ 2 (OR 0.61, 95% CI 0.53 to 0.71), or CHA(2)DS(2)-VASc scores of ≥ 2 (OR 0.60, 95% CI 0.53 to 0.68). CONCLUSIONS: In elderly patients with NVAF and elevated CHADS(2) or CHA(2)DS(2)-VASC scores, warfarin users exhibited lower rates of cerebrovascular events and mortality. However, warfarin use did not differ across risk strata, another example of the risk--treatment paradox in cardiovascular disease.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".