Prognostic significance of residual venous obstruction in patients with treated unprovoked deep vein thrombosis
Bibliographic record
Abstract
Residual venous obstruction (RVO) could improve the stratification of the risk of recurrence after unprovoked deep vein thrombosis (DVT), but results from clinical studies and study-level meta-analyses are conflicting. It was the objective of this analysis to determine if RVO is a valid predictor of recurrent venous thromboembolism (VTE) in patients with a first unprovoked DVT who had received at least three months of anticoagulant therapy. Individual patient data were obtained from the datasets of original studies, after a systematic search of electronic databases (Medline, Embase, Cochrane Library), supplemented by manual reviewing of the reference lists and contacting content experts. A multivariate, shared-frailty Cox model was used to calculate hazard ratios (HRs) for recurrent VTE, including, as covariates: RVO; age; sex; anticoagulation duration before RVO assessment; and anticoagulation continuation after RVO assessment. A total of 2,527 patients from 10 prospective studies were included. RVO was found in 1,380 patients (55.1%) after a median of six months from a first unprovoked DVT. Recurrent VTE occurred in 399 patients (15.8%) during a median follow-up of 23.3 months. After multivariate Cox analysis, RVO was independently associated with recurrent VTE (HR = 1.32, 95% confidence interval [CI]: 1.06-1.65). The association was stronger if RVO was detected early, i.e. at three months after DVT (HR = 2.17; 95% CI: 1.11-4.25), but non-significant if detected later, i.e. >6 months (HR = 1.19; 95% CI: 0.87-1.61). In conclusion, after a first unprovoked DVT, RVO is a weak overall predictor of recurrent VTE. The association is stronger if RVO is detected at an earlier time (3 months) after thrombosis.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".