Scoring Systems for Postthrombotic Syndrome
Bibliographic record
Abstract
Postthrombotic syndrome (PTS) is the most common long-term complication after deep vein thrombosis (DVT) and is associated with reduced quality of life. There is no single objective test to diagnose the presence of PTS and it is usually diagnosed on the basis of typical symptoms and signs in a limb previously affected by DVT. Scoring systems for PTS are primarily developed as research tools, but could possibly also be useful in the clinical setting. A main advantage of a good scoring system is standardization of the diagnostic process. An optimal scoring system should be both sensitive and specific for PTS, but this has been difficult to achieve because the symptoms and signs of PTS can be similar to other conditions leading to complaints in the lower limb. In an effort to standardize the definition of PTS, in 2009, the International Society on Thrombosis and Haemostasis Subcommittee on Control of Anticoagulation reviewed available scales and recommended use of the Villalta scale as the most appropriate measure to diagnose and grade the severity of PTS. The aim of this article is to review the existing scoring systems for PTS and to present our view on the advantages and disadvantages of these diagnostic tools.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".