D-dimer diagnostics: can I use any D-dimer assay? Bridging the knowledge-to-action gap
Bibliographic record
Abstract
A State of the Art lecture titled "D-dimer Diagnostics: Can I use any D-dimer assay? Bridging the Knowledge-to-Action gap" was presented at the International Society on Thrombosis and Haemostasis Congress in 2023, included in the session on the clinical impact of variability in commonly used coagulation assays. Here, we review the role of D-dimer, primarily in the outpatient diagnosis of patients with venous thromboembolism (VTE) when combined with clinical decision rules. We focus on the recent large management trials that have studied adjustments of VTE exclusion thresholds for D-dimer based on either prior clinical probability of VTE or patient age, and the resultant benefit of reduced imaging for VTE and improved diagnostic efficiency. In this context, we report on the significant variability between D-dimer results and the multiple D-dimer assays in use worldwide using data from international external quality assurance programs. This variability is particularly high at typical VTE exclusion thresholds. We discuss the potential clinical impact of D-dimer assay substitution on accuracy of diagnosis and risk stratification of patients with VTE. Finally, we summarize relevant new data on this topic presented during the 2023 International Society on Thrombosis and Haemostasis Congress and outline future priorities urgently needed to harmonize D-dimer results and reporting that will require international collaboration among multiple stakeholders with an overall goal to close this knowledge-to-action gap.
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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 | 0.053 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".