D‐dimer to rule out venous thromboembolism during pregnancy: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The usefulness of D-dimer measurement to rule out venous thromboembolism (VTE) during pregnancy is debated. OBJECTIVES: We performed a systematic review and meta-analysis to investigate the safety of D-dimer to rule out acute VTE in pregnant women with suspected pulmonary embolism and/or deep vein thrombosis. METHODS: Two reviewers independently identified studies through PubMed and Embase until June 2021, week 1. We supplemented our search by manually reviewing reference lists of all retrieved articles, clinicalTrials.gov, and reference literature. Prospective or retrospective studies in which a formal diagnostic algorithm was used to evaluate the ability of D-dimer to rule out VTE during pregnancy were eligible. RESULTS: We identified 665 references through systematic database and additional search strategies; 45 studies were retrieved in full, of which four were included, after applying exclusion criteria. Three studies were prospective, and one had a retrospective design. The 3-month thromboembolic rate in pregnant women left untreated after a negative D-dimer was 1/312 (0.32%; 95% CI, 0.06-1.83). The pooled estimate values were 99.5% for sensitivity (95% CI, 95.0-100.0; I², 0%) and 100% for negative predictive value (95% CI, 99.19-100.0; I², 0%). The prevalence of VTE and the yield of D-dimer were 7.4% (95% CI, 3.8-12; I², 83%) and 34.2% (95% CI, 15.9-55.23; I², 89%) respectively. CONCLUSION: Our results suggest that D-dimer allows to safely rule out VTE in pregnant women with suspected VTE and a disease prevalence consistent with a low/intermediate or unlikely pretest probability.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".