Using an age-dependent D-dimer cut-off value increases the number of older patients in whom deep vein thrombosis can be safely excluded
Bibliographic record
Abstract
BACKGROUND: D-dimer testing to rule out deep vein thrombosis is less useful in older patients because of a lower specificity. An age-adjusted D-dimer cut-off value increased the proportion of older patients (>50 years) in whom pulmonary embolism could be excluded. We retrospectively validated the efficacy of this cut-off combined with clinical probability for the exclusion of deep vein thrombosis. DESIGN AND METHODS: Five management study cohorts of 2818 consecutive outpatients with suspected deep vein thrombosis were used. Patients with non-high or unlikely probability of deep vein thrombosis were included in the analysis; four different D-dimer tests were used. The proportion of patients with a normal D-dimer test and the failure rates were calculated using the conventional (500 μg/L) and the age-adjusted D-dimer cut-off (patient's age x 10 μg/L in patients >50 years). RESULTS: In 1672 patients with non-high probability, deep vein thrombosis could be excluded in 850 (51%) patients with the age-adjusted cut-off value versus 707 (42%) patients with the conventional cut-off value. The failure rates were 7 (0.8; 95% confidence interval 0.3-1.7%) for the age-adjusted cut-off value and 5 (0.7%, 0.2-1.6%) for the conventional cut-off value. The absolute increase in patients in whom deep vein thrombosis could be ruled out using the age-adjusted cut-off value was largest in patients >70 years: 19% among patients with non-high probability. CONCLUSIONS: The age-adjusted cut-off of the D-dimer combined with clinical probability greatly increases the proportion of older patients in whom deep vein thrombosis can be safely excluded.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".