Performance and Head-to-Head Comparison of Three Clinical Models to Predict Occurrence of Postthrombotic Syndrome: A Validation Study
Bibliographic record
Abstract
OBJECTIVE: The SOX-PTS, Amin, and Méan models are three different clinical prediction scores stratifying the risk for postthrombotic syndrome (PTS) development in patients with acute deep vein thrombosis (DVT) of the lower limbs. Herein, we aimed to assess and compare these scores in the same cohort of patients. METHODS: We retrospectively applied the three scores in a cohort of 181 patients (196 limbs) who participated in the SAVER pilot trial for an acute DVT. Patients were stratified into PTS risk groups using positivity thresholds for high-risk patients as proposed in the derivation studies. All patients were assessed for PTS 6 months after index DVT using the Villalta scale. We calculated the predictive accuracy for PTS and area under receiver operating characteristic (AUROC) curve for each model. RESULTS: The Méan model was the most sensitive (sensitivity 87.7%; 95% confidence interval [CI]: 77.2-94.5) with the highest negative predictive value (87.5%; 95% CI: 76.8-94.4) for PTS. The SOX-PTS was the most specific score (specificity 97.5%; 95% CI: 92.7-99.5) with the highest positive predictive value (72.7%; 95% CI: 39.0-94.0). The SOX-PTS and Méan models performed well for PTS prediction (AUROC: 0.72; 95% CI: 0.65-0.80 and 0.74; 95% CI: 0.67-0.82), whereas the Amin model did not (AUROC: 0.58; 95% CI: 0.49-0.67). CONCLUSION: Our data support that the SOX-PTS and Méan models have good accuracy to stratify the risk for 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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".