Bibliographic record
Abstract
The postthrombotic syndrome (PTS) is a chronic complication of deep vein thrombosis (DVT) that imposes significant morbidity, reduces quality of life, and is costly. After DVT, 20% to 50% of patients will develop PTS, and up to 5% will develop severe PTS. The principal risk factors for PTS are anatomically extensive DVT, recurrent ipsilateral DVT, obesity, and older age. By preventing the initial DVT and DVT recurrence, primary and secondary prophylaxis of DVT will reduce occurrence of PTS. The effectiveness of elastic compression stockings (ECSs) for PTS prevention is controversial. Catheter-directed thrombolysis is not effective to prevent PTS overall but may prevent more severe forms of PTS and should be reserved for select patients with extensive thrombosis, recent symptoms onset, and low bleeding risk. For patients with established PTS, the cornerstone of management is ECS, exercise, and lifestyle modifications. Surgical or endovascular interventions may be considered in refractory cases. Because of a lack of effective therapies, new approaches to preventing and treating PTS are needed. This article uses a case-based approach to discuss risk factors for PTS after DVT, how to diagnose PTS, and available means to prevent and treat PTS, with a focus on new information in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".