Association Between Asymptomatic Proximal Deep Vein Thrombosis and Mortality in Acutely Ill Medical Patients
Bibliographic record
Abstract
Background Asymptomatic proximal deep vein thrombosis (DVT) is an end point frequently used to evaluate the efficacy of anticoagulant thromboprophylaxis in medical patients. Recently, the clinical relevance of asymptomatic DVT has been challenged. Methods and Results The objective of this study was to evaluate the relationship between asymptomatic proximal DVT and all‐cause mortality (ACM) using a cohort analysis of a randomized trial for the prevention of venous thromboembolism (VTE) in acutely ill medical patients. Patients who received at least 1 dose of study drug and had an adequate compression ultrasound examination of the legs on either day 10 or day 35 were categorized into 1 of 3 cohorts: no VTE, asymptomatic proximal DVT, or symptomatic DVT. Cox proportional hazards model, with adjustment for significant independent predictors of mortality, were used to compare the incidences of ACM. Of the 7036 patients, 6776 had no VTE, 236 had asymptomatic DVT, and 24 had symptomatic VTE. The incidence of ACM was 4.8% in patients without VTE. Both asymptomatic proximal DVT (mortality, 11.4%; hazard ratio [HR], 2.31; 95% CI, 1.52–3.51; P <0.0001) and symptomatic VTE (mortality, 29.2%; HR, 9.42; 95% CI, 4.18–21.20; P <0.0001) were independently associated with significant increases in ACM. The analysis was post hoc, and ultrasound results were not available for all patients. Adjustment for baseline variables significantly associated with ACM may not fully compensate for differences. Conclusions Asymptomatic proximal DVT is associated with higher ACM than no VTE and remains a relevant end point to evaluate the efficacy of anticoagulant thromboprophylaxis in medical patients. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT00571649.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".