Thrombolysis in Deep Vein Thrombosis: Is there still an Indication?
Bibliographic record
Abstract
The most accepted therapy for DVT consists of anticoagulation with unfractionated heparin or low molecular weight heparin, followed by variable duration oral anticoagulation but thrombolytic therapy has been proposed in addition to standard anticoagulation. This paper reviews the literature on post thrombotic syndrome, the natural history of vein patency after therapy, and we perform a systematic review, using accepted standards for meta-analysis, to determine the outcomes when thrombolytic therapy is used to treat DVT. We demonstrate that thrombolytic therapy for DVT results in a significant increase in the risk of major hemorrhage and a significant increase in the rate of vein patency. However, although thrombolytic therapy is advantageous over anticoagulation as measured by early vein patency, a benefit in terms of a reduction in PTS risk, is unproven. Our review also shows that there is no evidence that there is a difference in efficacy between thrombolytic agents or that local therapy differs from systemic therapy. Finally, the potential role of catheter directed therapy is unknown since appropriate trials have not been performed but it is reasonable to use catheter directed therapy in patients with phlegmasia cerulea dolens. We conclude that more work is needed to define the role of thrombolytic therapy but it is too early to abandon this therapeutic modality.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".