VIDAS D-Dimer in Combination with Clinical Pre-Test Probability to Rule out Pulmonary Embolism. A Systematic Review of the Management Outcome Studies.
Bibliographic record
Abstract
Abstract Background: Clinical outcome studies have shown that it is safe to withhold anticoagulant therapy in patients with suspected pulmonary embolism (PE) who have a negative D-dimer result and a low pre-test probability (PTP) either using a PTP model or clinical gestalt. Purpose: To assess the safety of the combination of a non-high PTP using the Wells or Geneva models with a negative VIDAS© D-dimer result to exclude PE. Data Source: A systematic literature search strategy was conducted using MEDLINE, EMBASE, the Cochrane Register of Controlled Trials and all EBM Reviews. Study Selection: Seven studies (6 prospective management studies and 1 randomized controlled trial) reporting failure rates at three months were included in the analysis. Non-high PTP was defined has “unlikely” or “low/intermediate” PTP using either, the Wells’ score, the Geneva, Revised Geneva Score, or gestalt estimation. Data extraction: Two reviewers independently extracted data onto standardized forms. Data Synthesis: A total of 5,622 patients with non-high PTP were assessed using the VIDAS© D-dimer. PE was ruled out by a negative VIDAS© D-dimer test in 40% (95% confidence intervals (CI) 38.7 to 41.2%) of patients. The three-month thromboembolic risk in patients left untreated was 0.14% (95% CI 0.05 to 0.4%). Table 1. Accuracy Indices Total non-high PTP and negative VIDAS© D-Dimer Wells’ “unlikely” PTP and negative VIDAS© D-dimer Geneva* “low/intermediate” and negative VIDAS© D-dimer Number of patients 5,622 2,017 3,208 Sensitivity (%, 95% CI) 99.7 (99.0– 99.9) 98.7 (96.2– 99.6) 100.0 (99.4–100) Specificity (%, 95% CI) 47.4 (46.0– 48.9) 57.3 (55.0– 59.6) 40.8 (38.9– 42.7) NPV (%, 95% CI) 99.9 (99.6– 100) 99.7 (99.1– 99.9) 100.0 (99.6– 100) Conclusion: The combination of a non-high PTP with a negative VIDAS© D-dimer result, effectively and safely exclude PE in an important proportion of outpatients with suspected PE.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".