Additional file 1 of Concomitant use of direct oral anticoagulants and interacting antiarrhythmic drugs and the risk of stroke and bleeding among patients with non-valvular atrial fibrillation: a multinational cohort study
Bibliographic record
Abstract
Additional file 1: Figure S1. Study design. Figure S2. Flowchart illustrating the construction of the study cohort. Figure S3. Cumulative incidences of the study outcomes. Table S1. ICD-10 codes for the definition of ischemic stroke and major bleeding. Table S2. Baseline characteristics of patients in the CPRD. Table S3. Baseline characteristics of patients in the RAMQ. Table S4. Reasons for censoring. Table S5. Risk of ischemic stroke associated with concomitant use of DOACs and interacting antiarrhythmics compared with concomitant use of DOACs and non-interacting antiarrhythmics among patients with NVAF (stratification by demographics). Table S6. Risk of ischemic stroke associated with concomitant use of DOACs and interacting antiarrhythmics compared with concomitant use of DOACs and non-interacting antiarrhythmics (stratification by baseline risk, individual DOACs, and type of DOAC use). Table S7. Risk of major bleeding associated with concomitant use of DOACs and interacting antiarrhythmics compared with concomitant use of DOACs and non-interacting antiarrhythmics (stratification by demographics). Table S8. Risk of major bleeding associated with concomitant use of DOACs and interacting antiarrhythmics compared with concomitant use of DOACs and non-interacting antiarrhythmics (stratification by baseline risk, individual DOACs, and type of DOAC use). Table S9. Risk of ischemic stroke associated with concomitant use of DOACs and interacting antiarrhythmics compared with concomitant use of DOACs and non-interacting antiarrhythmics (sensitivity analyses). Table S10. Risk of major bleeding associated with concomitant use of DOACs and interacting antiarrhythmics compared with concomitant use of DOACs and non-interacting antiarrhythmics (sensitivity analyses).
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.656 | 0.041 |
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".