Plasma D-Dimers in the Diagnosis of Venous Thromboembolism
Bibliographic record
Abstract
Clinical suspicion for venous thromboembolism (VTE) mandates objective testing to confirm or exclude the diagnosis. However, current imaging modalities are imperfect because of a small but important risk of complications with invasive techniques or limited sensitivity with noninvasive ones. A diagnostic tool for VTE is needed that is noninvasive and highly accurate, allowing immediate treatment decisions to be made in most cases. Plasma D-dimers (D-ds), specific cross-linked fibrin derivatives, partially fulfill these criteria in that they are sensitive markers for thrombosis but lack specificity. They therefore cannot be used to make a positive diagnosis of VTE; however, they generally have high negative predictive value and are useful as an exclusionary test, a potentially important role given that VTE is eventually ruled out in most patients investigated. Clinical management studies are clarifying the role of D-ds in the diagnostic paradigm of VTE: negative ultrasound and D-d findings obviate the need for serial imaging in suspected deep vein thrombosis, and anticoagulant therapy can be safely withheld in patients with non-high clinical suspicion for pulmonary embolism and non-high probability ventilation perfusion scan if D-d test results are negative. More recently, the combination of a negative SimpliRED (AGEN Biomedical Ltd, Brisbane, Australia) D-d result and low clinical suspicion derived using a formal scoring system has been shown to exclude deep vein thrombosis and pulmonary embolism and to obviate the need for imaging. Several different D-d assays are now available, and clinicians should be aware of the performance characteristics of the test used before incorporation into diagnostic algorithms as these will differ between assays, and the results of clinical management studies cannot necessarily be safely extrapolated to assays other than those specifically evaluated. If alternative assays are to be substituted, these should consistently have been shown to possess equivalent or greater sensitivity.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".