Current Challenges in Diagnosis of Venous Thromboembolism
Bibliographic record
Abstract
In patients with suspected venous thromboembolism, the goal is to accurately and rapidly identify those with and without thrombosis. Failure to diagnose venous thromboembolism (VTE) can lead to fatal pulmonary embolism (PE), and unnecessary anticoagulation can cause avoidable bleeding. The adoption of a structured approach to VTE diagnosis, that includes clinical prediction rules, D-dimer testing and non-invasive imaging modalities, has enabled rapid, cost-effective and accurate VTE diagnosis, but problems still persist. First, with increased reliance on imaging and widespread use of sensitive multidetector computed tomography (CT) scanners, there is a potential for overdiagnosis of VTE. Second, the optimal strategy for diagnosing recurrent leg deep venous thrombosis remains unclear as is that for venous thrombosis at unusual sites. Third, the conventional diagnostic approach is inefficient in that it is unable to exclude VTE in high-risk patients. In this review, we outline pragmatic approaches for the clinician faced with difficult VTE diagnostic cases. In addition to discussing the principles of the current diagnostic framework, we explore the diagnostic approach to recurrent VTE, isolated distal deep-vein thrombosis (DVT), pregnancy associated VTE, subsegmental PE, and VTE diagnosis in complex medical patients (including those with impaired renal function).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".