ANMCO position paper ‘Appropriateness of prescribing direct oral anticoagulants in stroke and systemic thromboembolism prevention in adult patients with non-valvular atrial fibrillation’
Bibliographic record
Abstract
The appropriateness of prescribing direct oral anticoagulants [dabigatran, rivaroxaban, apixaban, and edoxaban (DOACs)] is regulated on the criteria established in Phase III trials. These criteria are reported in the summary of the product characteristics of the four DOACs. In clinical practice, prescriptions are not always in compliance with established indications. In particular, the use of lower doses than those recommended in drug data sheets is not uncommon. Literature data show that the inappropriate prescription of reduced doses causes drug underexposure and up to a three-fold increase in the risk of stroke/ischaemic transient attack, systemic thromboembolism, and hospitalization. Possible causes of the deviation between the dose that should be prescribed and that prescribed in the real world include erroneous prescription, an overstated haemorrhagic risk perception, and the presence of frail and complex patients in clinical practice who were not included in pivotal trials, which makes it difficult to apply study results to the real world. For these reasons, we summarize DOAC indications and contraindications. We also suggest the appropriate use of DOACs in common clinical scenarios, in accordance with what international guidelines and national and international health regulatory bodies recommend.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".