Bibliographic record
Abstract
CONTEXT: Outpatients with suspected deep vein thrombosis (DVT) have nonspecific signs and symptoms. Missed DVT diagnosis may result in fatal pulmonary embolism. Since many patients may have DVT, a selective and efficient diagnostic process is needed. OBJECTIVE: To systematically review trials that determined the prevalence of DVT using clinical prediction rules either with or without D-dimer, for the diagnosis of DVT. DATA SOURCES: English- and French-language studies were identified from MEDLINE from 1990 to July 2004 and supplemented by a review of all relevant bibliographies. STUDY SELECTION: We included studies that prospectively enrolled consecutive, unselected outpatients with suspected DVT and applied clinical prediction rules before D-dimer testing or diagnostic imaging. All studies included sufficient information to allow the calculation of the prevalence of DVT for at least 1 of the 3 clinical probability estimates (low, moderate, or high). We required that patients be followed up for a minimum 3-month period. Unless the clinical model incorporated prior DVT, studies were excluded if patients with a history of prior DVT were enrolled. DATA EXTRACTION: Two reviewers independently reviewed and abstracted data for estimating the prevalence of DVT, sensitivity, specificity, and likelihood ratios (LRs) of D-dimer in each of the 3 clinical probability estimates. Data for the D-dimer in all studies were pooled and analyzed as high-sensitivity/low-specificity test or a moderate-sensitivity/moderate-specificity test. DATA SYNTHESIS: Fourteen studies involving more than 8000 patients used 1 clinical prediction rule for diagnosing DVT, of which 11 incorporated D-dimer testing in the diagnostic algorithm. The prevalence of DVT in the low, moderate, and high clinical probability groups was 5.0% (95% CI, 4.0%-8.0%), 17% (95% CI, 13%-23%), and 53% (95% CI, 44%-61%), respectively. The overall prevalence of DVT was 19% (95% CI, 16%-23%). Pooling all studies, the sensitivity, specificity, and negative LRs of D-dimer testing in the low probability group were 88% (95% CI, 81%-92%), 72% (95% CI, 65%-78%), and 0.18% (95% CI, 0.12-0.18); in the moderate probability group: 90% (95% CI, 80%-95%), 58% (95% CI, 49%-67%), and 0.19% (95% CI, 0.11-0.32); and in the high probability group: 92% (95% CI, 85%-96%), 45% (95% CI, 37%-52%), and 0.16% (95% CI, 0.09-0.30). The LRs for a normal result on a high or moderately sensitive D-dimer assay among patients with: (1) low clinical suspicion were 0.10 (95% CI, 0.03-0.37) and 0.20 (95% CI, 0.12-0.31); (2) moderate clinical suspicion were 0.05 (95% CI, 0.01-0.21) and 0.23 (95% CI, 0.13-0.39); and (3) high clinical suspicion were 0.07 (95% CI, 0.03-0.18) and 0.15 (95% CI, 0.10-0.38). CONCLUSIONS: Diagnostic accuracy for DVT improves when clinical probability is estimated before diagnostic tests. Patients with low clinical probability on the predictive rule have prevalence of DVT of less than 5%. In low-probability patients with negative D-dimer results, diagnosis of DVT can be excluded without ultrasound; in patients with high clinical suspicion for DVT, results should not affect clinical decisions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".