Vitamin K antagonist-experienced patients with a history of stroke/transient ischaemic attack who switched from warfarin to dabigatran increased their rate of recurrent stroke/transient ischaemic attack compared with those on warfarin
Bibliographic record
Abstract
Commentary on : Larsen TB, Rasmussen LH, Gorst-Rasmussen A, et al. Dabigatran and warfarin for secondary prevention of stroke in atrial fibrillation patients: a nationwide cohort study. Am J Med 2014;127:1172–8[OpenUrl][1][CrossRef][2][PubMed][3]. Randomised trials have shown that patients with atrial fibrillation (AF) who are treated with a non-vitamin K antagonist oral anticoagulant (NOAC), compared with warfarin, have similar or lower rates of stroke and major bleeding, markedly reduced rates of intracranial bleeding and a consistent pattern of reduced mortality.1 Dabigatran 150 mg two times a day is the only NOAC that can significantly reduce ischaemic stroke compared with warfarin and can produce even greater ischaemic stroke reduction in patients with prior stroke.2 European guidelines endorse the preferential … [1]: {openurl}?query=rft.jtitle%253DAm%2BJ%2BMed%26rft.volume%253D127%26rft.spage%253D1172%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.amjmed.2014.07.023%26rft_id%253Dinfo%253Apmid%252F25193361%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1016/j.amjmed.2014.07.023&link_type=DOI [3]: /lookup/external-ref?access_num=25193361&link_type=MED&atom=%2Febmed%2F20%2F3%2F117.atom
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".