Decrease in sensitivity of D-dimer for acute venous thromboembolism after starting anticoagulant therapy
Bibliographic record
Abstract
D-dimer testing is useful for the exclusion of acute venous thromboembolism (VTE). Anticoagulant therapy is expected to reduce D-dimer levels in patients with thrombosis and, consequently, it may not be safe to use D-dimer levels to exclude VTE after anticoagulant therapy has been started. The objectives of this study were to estimate the decrease in D-dimer levels after 24 h of heparin therapy and, applying this estimate to the results of a recent study, to calculate the expected reduction in sensitivity. Using pre-defined criteria, we first performed a literature review to determine whether, and by how much, D-dimer levels decrease within 24 h of starting heparin therapy in patients with acute VTE. Using D-dimer levels that were measured in a prospective study of patients with confirmed deep vein thrombosis and/or pulmonary embolism as baselines, we then determined the change in sensitivity (and specificity) that would result from the fall in D-dimer levels that the literature review suggested would have occurred after 24 h of heparin therapy. On the basis of the literature review, we calculated that mean D-dimer levels decrease by 25%, 24 h after starting heparin therapy in patients with acute VTE. This 25% decrease in D-dimer levels resulted in a decrease in sensitivity from 95.6% (95% confidence interval, 90.0-98.6) to 89.4% (95% confidence interval, 83.7-95.1). There is a decrease in D-dimer levels in patients with acute VTE 24 h after starting heparin therapy that is expected to result in a clinically important drop in 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 | 0.022 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".