Influence of hereditary or acquired thrombophilias on the treatment of venous thromboembolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: Deficiency of antithrombin, protein C, and protein S increases the risk of a first venous thromboembolism (VTE) by at least 10-fold and is rare (i.e., <0.5% of population), whereas factor V Leiden and the prothrombin G20210A gene increase this risk by 2-5-fold and are common (2-5% of whites). Antiphospholipid antibodies are considered acquired thrombophilic states. Testing for these abnormalities is widespread. This review will consider if the results of testing should influence how patients with VTE are treated. RECENT FINDINGS: There are no randomized trials that have compared testing for thrombophilia with no testing; consequently, current assessments of whether testing should influence treatment of VTE are based on indirect evidence. Observational studies indicate that anticoagulants are equally effective in patients with and without thrombophilia; therefore, the presence of thrombophilia should not influence the choice of anticoagulant or the intensity of therapy. A lupus anticoagulant, however, can complicate monitoring of vitamin K antagonist therapy. The risk of recurrent VTE after stopping anticoagulant therapy may be higher in patients with thrombophilia, but not enough to influence whether anticoagulants should be stopped at 3 months or continued indefinitely. SUMMARY: Thrombophilia should rarely influence the treatment of VTE. Therefore, routine thrombophilia testing of patients with VTE is not indicated as a way to guide treatment decisions.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".