Direct oral anticoagulant use in special populations
Bibliographic record
Abstract
PURPOSE OF REVIEW: The pivotal phase III trials demonstrating efficacy and safety of direct oral anticoagulants (DOACs) in the treatment of venous thromboembolism (VTE) or nonvalvular atrial fibrillation (NVAF) excluded patients with important and common comorbidities, including obesity, advanced chronic kidney disease, cirrhosis, cancer and antiphospholipid antibody syndrome. Despite the lack of large prospective randomized control trials in these patient populations, the use of DOACs has led to a wealth of efficacy and safety data within these groups. RECENT FINDINGS: Retrospective studies, meta-analyses, national databases and pharmacokinetic data have shed light on the efficacy and safety of DOACs in these patient populations. Although DOACs should be avoided in those with high-risk triple positive antiphospholipid antibody syndrome, advanced cirrhosis, advanced kidney disease and intact gastrointestinal cancers, and used with caution in genitourinary cancers, their use extends beyond the inclusion criteria of the initial randomized control trials. SUMMARY: DOACs have revolutionized anticoagulant management and have become the cornerstone for VTE treatment and stroke prevention in NVAF. The decision to use DOACs must be individualized. Patient preference, underlying comorbidities and informed consent must always be considered when selecting the most appropriate anticoagulant.
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
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".