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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".