The history and historical treatments of deep vein thrombosis: toward the era of new anticoagulants
Bibliographic record
Abstract
Deep vein thrombosis (DVT) is a common disease. In a review published in 2013, we provided a comprehensive history of DVT management, with particular emphasis on treatments that were later introduced or abandoned. At that time, the history of direct oral anticoagulants was still emerging, and we chose not to delve into this topic at that point. Twelve years later, direct oral anticoagulants have become the standard of care for DVT treatment, not only revolutionizing management by simplifying therapy but also influencing the intensity and duration of anticoagulant treatment. This new historical review focuses on aspects of DVT treatment that were not covered previously, including the quest of the development of safer and more user-friendly alternatives to older anticoagulants, the evolving history of anticoagulant treatment duration and intensity, as well as how studies have influenced the American College of Chest Physicians guidelines. Although great successes have been achieved, this review will highlight that anticoagulation philosopher's stone has yet to be found, if ever found. Nevertheless, recent data on inhibitors of factor (F)XI/XIa might suggest that we are approaching closer to safer anticoagulants. Looking ahead, in the absence of possibility for a single universal treatment for DVT, the future of DVT treatment will probably lie both on the development of newer anticoagulants and on the development of artificial intelligence, which could offer individualized treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".