New and known predictors of the postthrombotic syndrome: A subanalysis of the ATTRACT trial
Bibliographic record
Abstract
Introduction: Postthrombotic syndrome (PTS) remains associated with significant clinical and economic burden. This study aimed to investigate known and novel predictors of the development of PTS in participants of the ATTRACT (Acute Venous Thrombosis: Thrombus Removal With Adjunctive Catheter-Directed Thrombolysis) trial. Methods: We used multivariable logistic regression to identify baseline and postbaseline factors that were predictive of the development of PTS during study follow-up, as defined by a Villalta score of 5 or greater or the development of a venous ulcer from 6 to 24 months after enrollment. Results: Among 691 patients in the study cohort (all had proximal deep vein thrombosis [DVT] that extended above the popliteal vein, of which 57% had iliofemoral DVT), 47% developed PTS. Further, we identified that Villalta score at baseline (odds ratio [OR], 1.09 [95% confidence interval [CI], 1.05-1.13] per one-unit increase) and employment status (unemployed due to disability: OR, 3.31 [95% CI, 1.72-6.35] vs. employed more than 35 hours per week) were predictive of PTS. In terms of postbaseline predictors, leg pain severity at day 10 (OR, 1.28 [95% CI, 1.13-1.45] per 1-point increase in a 7-point scale) predicted PTS. Also, patients receiving rivaroxaban on day 10 following randomization had lower rates of PTS (OR, 0.53 [95% CI, 0.33-0.86]) than patients on warfarin. Conclusions: Novel predictors for PTS identified in our study include baseline Villalta score, leg pain severity at 10 days, and unemployed due to disability. Our findings also suggest that the initial choice of anticoagulant to treat DVT may have an impact on the development 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".