Lack of consistency in the relationship between asymptomatic DVT detected by venography and symptomatic VTE in thromboprophylaxis trials
Bibliographic record
Abstract
Asymptomatic deep-vein thrombosis (DVT) detected by mandatory venography, a surrogate outcome, comprises most of the efficacy outcome events in recent thromboprophylaxis trials. The validity of this surrogate to estimate trade-off between thrombotic and bleeding events in these clinical trials requires a consistent relationship between asymptomatic DVT and symptomatic venous thromboembolism (VTE). In this systematic review of high quality VTE prevention trials, we examined the consistency of the ratios of asymptomatic DVT to symptomatic VTE across a broad range of indications. Studies were identified from citations listed in the chapters on VTE prevention in the antithrombotic guidelines by the American College of Chest Physicians, 2012. A study was eligible if it: 1) was a randomised trial comparing an anticoagulant with standard of care; 2) included at least 500 participants; 3) reported asymptomatic or all DVT rates; and 4) reported symptomatic VTE rates. Of the 26 eligible trials, 19 trials were conducted in orthopaedic patients, five in general surgery patients and two in general medical patients. The overall median rates (ranges) for asymptomatic DVT and symptomatic VTE were 9.11 % (0.75 to 54.87 %) and 0.49 % (0.00 to 3.10 %), respectively. The median ratio was 14.53, with a wide range (2.75 to 103.86). Wide variability in the ratios persisted despite indication- and anticoagulant-specific analyses. In VTE prevention trials of alternative anticoagulants, the wide variability in the ratios of asymptomatic DVT to symptomatic VTE precludes judging the trade-off between thrombotic and bleeding events on the basis of composite outcomes dominated by venographic DVT.
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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 | 0.455 | 0.732 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".