Frequency and determinants of the postthrombotic syndrome after venous thromboembolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: Postthrombotic syndrome (PTS) is the most common complication of deep venous thrombosis (DVT). Identifying which patients are at high risk of developing PTS would help improve the management of patients with DVT and allow physicians to provide patients with individualized information on their expected prognosis. This review discusses the knowledge gained from key studies over the last decade on the incidence and determinants of PTS, with special emphasis on published studies from the last 2 years. RECENT FINDINGS: About a third to half of DVT patients will develop PTS, in most cases within 1-2 years of acute DVT. Important risk factors for PTS appear to be ipsilateral recurrence of DVT, poor quality of initial anticoagulation for the treatment of DVT and increased body mass index. SUMMARY: Preventing DVT recurrence by providing adequate intensity and duration of anticoagulation for the initial DVT and using effective thromboprophylaxis in high-risk settings is likely to reduce the frequency of PTS. Despite some advances in identifying risk factors for PTS, however, it is still not possible to reliably predict an individual patient's risk of developing PTS after an episode of DVT. Further studies of clinical determinants and biological markers of increased risk of PTS are needed to ultimately improve long-term prognosis after DVT.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".