Update in the diagnosis of deep-vein thrombosis and pulmonary embolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: Diagnostic strategies for venous thromboembolism must both accurately diagnose thrombus when present, and safely exclude it when absent. This review summarizes recent data on diagnostic strategies for venous thromboembolism. RECENT FINDINGS: Noninvasive diagnostic strategies have emerged to limit the need for invasive testing for deep-vein thrombosis and pulmonary embolism. D-Dimer testing combined with clinical assessment can be used to safely exclude deep vein thrombosis, limiting the need for further testing. Extended lower limb ultrasonography also shows promise although requires further data. Spiral computed tomography has become widely used for the diagnosis of pulmonary embolism. Evidence either for the use of single-detector spiral computed tomography combined with ultrasound or for multidetector spiral computed tomography as a safe and stand-alone test, for the purpose of excluding pulmonary embolism, is finally catching up with current practice. SUMMARY: Invasive testing for venous thromboembolism can be safely avoided in the majority of patients, using diagnostic strategies combining noninvasive tests. Initial evidence suggests that multidetector spiral computed tomography is a safe stand-alone test for pulmonary embolism. Local cost and expertise with separate diagnostic tests will influence the appropriate choice of diagnostic strategies for venous thromboembolism at individual institutions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".