Post‐thrombotic syndrome: prevalence, prognostication and need for progress
Bibliographic record
Abstract
Despite considerable progress in the diagnosis and treatment of deep-vein thrombosis (DVT) of the lower extremities, one of every 2-3 patients will develop within 2 years post-thrombotic sequelae, which are severe in approximately 10% of cases and produce considerable socio-economic consequences. Among factors potentially related to the development of the post-thrombotic syndrome (PTS) are older age, obesity, a history of previous ipsilateral DVT, iliac-femoral location of the current thrombosis, failure to promptly recover from the acute symptoms and insufficient quality of oral anticoagulant therapy. Based on recent findings, the lack of vein recanalization within the first 6 months after DVT appears to be an important predictor of PTS, while the role of venous reflux is controversial. According to the results of recent clinical studies, the prompt administration of adequate compression elastic stockings in patients with symptomatic DVT has the potential to halve the frequency of PTS, and when carefully supervised and instructed to wear proper elastic stockings, more than 50% of patients have the potential to either remain stable or improve during long-term follow-up. Nevertheless, due to limitations in current therapies, the management of PTS is demanding and often frustrating. Further research is required to optimize the prevention and management of this common and burdensome complication of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 0.000 |
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".