Development and validation of a tool for patient reporting of symptoms and signs of the post-thrombotic syndrome
Bibliographic record
Abstract
Post-thrombotic syndrome (PTS) is a long-term complication of deep-vein thrombosis (DVT). The Villalta scale is the recommended tool for diagnosing PTS, but requires a clinician's assessment in addition to patient self-assessment. In the present study, we validated a self-administered tool for patient reporting of leg symptoms and signs as a mean to assess PTS. We first validated a form for patient self-reported Villalta (PRV1), then developed and validated a visually assisted form (PRV2). The validity of PRV1 and PRV2 was assessed in patients diagnosed with DVT between 2004 and 2012. Median time from DVT to inclusion was 5.1 and 3.5 years for PRV1 (n=162) and PRV2 (n=94), respectively. Patients were requested to complete the PRV form before a scheduled visit. PTS diagnosed by the original Villalta scale during the visit served as the reference method. PRV1 showed only moderate agreement for diagnosing PTS compared with the original Villalta scale (kappa agreement 0.60, 95% CI 0.48-0.72), whereas PRV2 showed very good agreement (0.82, 95% CI 0.71-0.94). In the validation of PRV2, PTS was diagnosed in 54 (57%) patients according to the original Villalta scale and in 60 (64%) by PRV2. The sensitivity of PRV2 to detect PTS was 98% and the specificity was 83%. We conclude that the visually assisted form for PRV is a valid and sensitive tool for diagnosing PTS. Such a tool could be applied in further clinical studies of PTS, making studies less resource demanding by reducing the need for in-person clinic visits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".