Comparison of clinical probability-adjusted D-dimer and age-adjusted D-dimer interpretation to exclude venous thromboembolism
Bibliographic record
Abstract
Two new strategies for interpreting D-dimer results have been proposed: i) using a progressively higher D-dimer threshold with increasing age (age-adjusted strategy) and ii) using a D-dimer threshold in patients with low clinical probability that is twice the threshold used in patients with moderate clinical probability (clinical probability-adjusted strategy). Our objective was to compare the diagnostic accuracy of age-adjusted and clinical probability-adjusted D-dimer interpretation in patients with a low or moderate clinical probability of venous thromboembolism (VTE). We performed a retrospective analysis of clinical data and blood samples from two prospective studies. We compared the negative predictive value (NPV) for VTE, and the proportion of patients with a negative D-dimer result, using two D-dimer interpretation strategies: the age-adjusted strategy, which uses a progressively higher D-dimer threshold with increasing age over 50 years (age in years × 10 µg/L FEU); and the clinical probability-adjusted strategy which uses a D-dimer threshold of 1000 µg/L FEU in patients with low clinical probability and 500 µg/L FEU in patients with moderate clinical probability. A total of 1649 outpatients with low or moderate clinical probability for a first suspected deep vein thrombosis or pulmonary embolism were included. The NPV of both the clinical probability-adjusted strategy (99.7 %) and the age-adjusted strategy (99.6 %) were similar. However, the proportion of patients with a negative result was greater with the clinical probability-adjusted strategy (56.1 % vs, 50.9 %; difference 5.2 %; 95 % CI 3.5 % to 6.8 %). These findings suggest that clinical probability-adjusted D-dimer interpretation is a better way of interpreting D-dimer results compared to age-adjusted interpretation.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".