Exploring the Villalta scale to capture postthrombotic syndrome using alternative approaches: A subanalysis of the ATTRACT trial
Bibliographic record
Abstract
Background Clinical trials that evaluated interventions to prevent postthrombotic syndrome (PTS) used the Villalta scale (VS) to define PTS, but there is a lack of consistency in its use. Objectives This study aimed to improve the ability to identify patients with clinically meaningful PTS after DVT in participants of the ATTRACT trial. Methods We conducted a post hoc exploratory analysis of 691 patients from the ATTRACT study, a randomized trial evaluating the effectiveness of pharmacomechanical thrombolysis to prevent PTS in proximal deep vein thrombosis. We compared 8 VS approaches to classify patients with or without PTS in terms of their ability to discriminate between those with poorer vs better venous disease-specific quality of life (Venous Insufficiency Epidemiological and Economic Study Quality of Life [VEINES-QOL]) between 6- and 24-months follow-up. The difference in the average area under the fitted curve of VEINES-QOL scores between PTS and no PTS ( Δ A U C ¯ ) were compared among approaches. Results For any PTS (a single VS score ≥5), approaches 1 to 3 had similar Δ A U C ¯ (−21.2, −23.7, −22.0, respectively). Adjusting the VS for contralateral chronic venous insufficiency (CVI) or restricting to patients without baseline CVI (approaches 7 and 8) did not improve Δ A U C ¯ (−13.6, −19.9, respectively; P >.01). For moderate-to-severe PTS (a single VS score ≥10), approaches 5 and 6 requiring 2 positive assessments had greater but not statistically significant Δ A U C ¯ than approach 4, using one single positive assessment (−31.7, −31.0, −25.5, respectively; P >.01). Conclusion A single VS score of ≥ 5 reliably distinguishes patients with clinically meaningful PTS as assessed by impact on QOL and is preferred because of greater convenience (only one assessment needed). Alternative methods to define PTS (ie, adjusting for CVI) do not improve the scale's ability to identify clinically meaningful PTS.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".