Abstract 13225: Which Oral Anti-Coagulant Do Patients Prefer for Stroke Prevention in Non-Valvular Atrial Fibrillation?
Bibliographic record
Abstract
Introduction: There are various oral anticoagulants available for stroke prevention in patients suffering from non-valvular atrial fibrillation (NVAF) with some drug-related variations in clinical profile and non-clinical attributes. Patient preferences should be taken into account in anticoagulant prescription decisions. Hypothesis: Patient valuation of different anticoagulant characteristics in stroke prevention allows for meaningful comparison of the non-VKA oral anticoagulants (NOACs; apixaban, dabigatran, edoxaban, rivaroxaban) and Vitamin K Antagonist (VKA, ie. warfarin). Methods: Multi-criteria decision analysis was developed with 5 clinical and 3 non-clinical criteria. Criteria weights were defined using results from two discrete choice experiments (DCEs). The evaluation model contained more fine-grained events than the DCEs, and therefore preference weights from DCEs needed to be distributed to the evaluation criteria. The weights were distributed according to event fatality rates, which were obtained from the RE-LY trial that compared dabigatran to warfarin. An additive model was used to combine treatment performance with the weights to estimate the overall value of each oral anticoagulant. Probabilistic and structural sensitivity analyses were performed. Results: Dabigatran obtained the highest overall value (see Figure: weighted contribution of different evaluation criteria to the overall value of five oral anticoagulants) and the highest first rank probability (88%) in the probabilistic sensitivity analysis. Rivaroxaban performed worse than the other NOACs, but better than VKA (both with 0% first rank probability). The results were insensitive to removing availability of reversal agent from the model. Conclusions: Patient preferences on treatment characteristics allows to discriminate oral anticoagulants for stroke prevention in NVAF, with dabigatran ranking highest and warfarin lowest.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".