External Validation of the Patient-Reported Villalta Scale for the Diagnosis of Postthrombotic Syndrome
Bibliographic record
Abstract
INTRODUCTION: The Villalta scale is the endorsed tool to diagnose and grade the severity of postthrombotic syndrome (PTS); however, assessing presence and severity of PTS is time-consuming and relies on both the clinician and patient's assessments. The patient-reported Villalta scale version 2 (PRV2) is a visually assisted form that enables patients to self-assess presence and severity of PTS. Herein, we report on external validation of this tool. METHODS: We assessed the agreement and kappa values of PRV2 to diagnose and assess severity of PTS compared with the original Villalta score in a cohort of 181 patients (196 limbs) who participated in the SAVER pilot randomized control trial. Presence of PTS was defined as PRV2 ≥5 or a Villalta score ≥5. RESULTS: PTS prevalence was 42% using PRV2 and 33% using the Villalta scale. The corresponding kappa and percentage agreement were 0.60 (95% confidence interval [CI]: 0.49-0.71) and 81% (95% CI: 76-87), respectively. Kappa values and percentage agreements between PRV2 and Villalta scale increased with increasing severity of PTS. The sensitivity of PRV2 to detect PTS of any severity was 84% (95% CI: 73-92) with a specificity of 79% (95% CI: 71-86). CONCLUSION: We conclude that the PRV2 is an acceptable tool for diagnosing and grading the severity of 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 | 0.034 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".