Clinical Studies with Anticoagulants that Have Changed Clinical Practice
Bibliographic record
Abstract
Anticoagulant therapy is the cornerstone of treatment and prevention of arterial and venous thromboembolism. Taking a historical perspective, starting in the 1960s, and progressing through to 2022, we discuss key clinical trials of anticoagulants that have changed clinical practice, and examine obstacles encountered in bringing these anticoagulants to the clinic. The design of some of the early studies that shaped clinical practice was poor by current standards, but their results were influential because nothing better was available. Both heparin and vitamin K antagonists had been in clinical use for several decades before well-designed trials in the 1980s optimized their dosing and enhanced their safety and efficacy. Low-molecular-weight heparin then replaced unfractionated heparin because it had a more predictable dose-response and a longer half-life, thereby allowing it to be used conveniently in out-of-hospital settings. More recently, direct oral anticoagulants became the oral anticoagulants of choice for most indications because they were shown to be at least as safe and effective as vitamin K antagonists when used in fixed doses without the need for laboratory monitoring. The design of the trials that led to the approval of the direct oral anticoagulants was excellent, but further studies are required to optimize their dosing in selected patients who were underrepresented in these trials.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".