P251Asymptomatic deep vein thrombosis in acutely ill medical patients: insights from the APEX trial
Bibliographic record
Abstract
Aim: Asymptomatic deep vein thrombosis (DVT) diagnosed with compression ultrasound (CUS) is a common endpoint in trials assessing the efficacy of anticoagulants to prevent venous thromboembolism (VTE), but the relationship of asymptomatic thrombus to mortality remains uncertain. Methods: In the APEX trial, 7,513 acutely ill hospitalized medical patients were randomly assigned to extended-duration betrixaban (35–42 days) or enoxaparin (10±4 days). Asymptomatic deep vein thrombosis (DVT) was assessed once with a compression ultrasound (CUS) between day 35 and 47, and mortality was assessed through 77 days. Results: A total of 309 asymptomatic DVTs were detected through CUS. Of these, 133 (4.27%) subjects were in the betrixaban group, and 176 (5.55%) subjects were in the enoxaparin group (RR = 0.77, 95% CI = 0.62–0.97, p=0.025, NNT = 79). With respect to all-cause mortality due to cardiovascular diseases, non-cardiovascular diseases and unknown causes, the number of the deaths was 5 (1.67%), 4 (1.34%), 1 (0.33%) in the asymptomatic DVT group and 25 (0.42%), 33 (0.56%), 11 (0.19%) in the no DVT group, respectively. Subjects with an asymptomatic DVT had an almost three-fold increase in the risk of all-cause mortality compared to subjects without DVT (HR = 2.87, 95% CI = 1.48, 5.57, p=0.001). Greater thrombus burden on CUS was associated with higher mortality (p=0.019).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".