Do anticoagulants improve survival in patients presenting with venous thromboembolism?
Bibliographic record
Abstract
Anticoagulants have been available since around 1940 and have become the standard of treatment for venous thromboembolism (VTE) for over four decades. However, as with other treatments which became established before the evidence-based era, there is a paucity of evidence from randomized controlled trials validating their effectiveness in preventing the most feared complication of VTE, recurrent fatal pulmonary embolism (PE). Only two such trials have been performed, the results of which conflict. The bulk of data supporting their use are derived from three sources. First, studies of thromboprophylaxis, and comparisons of shorter and longer courses of anticoagulants in high-risk patients with established VTE have clearly demonstrated their effectiveness in primary and late secondary prevention. Given that heparin has an immediate onset of action, anticoagulants should therefore also be effective in early secondary prevention, the proposed mechanism of action in the acute treatment of VTE. Secondly, studies of inadequately treated patients have consistently shown higher recurrence rates than in those adequately treated. Finally, comparisons of outcomes in untreated and treated historical series, and of untreated historical series to treated series in the modern era have shown substantially lower rates of fatal PE in anticoagulated patients. Because these differences are so marked, harmonize with our current understanding of the mechanism of action of anticoagulants and are supported by other evidence, it is much more likely that they at least partly reflect the effectiveness of anticoagulants as opposed to being explicable purely in terms of accumulated biases and a changing distribution of disease severity.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".