Diagnosis of deep vein thrombosis with D-dimer adjusted to clinical probability: prospective diagnostic management study
Bibliographic record
Abstract
Abstract Objective To evaluate the safety and efficiency of a diagnostic algorithm for deep vein thrombosis (DVT) that uses clinical pretest probability based D-dimer thresholds to exclude DVT. Design Prospective diagnostic management study. Setting University based emergency departments or outpatient clinics in Canada. Participants Patients with symptoms or signs of DVT. Intervention DVT was considered excluded without further testing by Wells low clinical pretest probability and D-dimer <1000 ng/mL or Wells moderate clinical pretest probability and D-dimer <500 ng/mL. All other patients had proximal ultrasound imaging. Repeat proximal ultrasonography was restricted to patients with initially negative ultrasonography, low or moderate clinical pretest probability, and D-dimer >3000 ng/mL or high clinical pretest probability and D-dimer >1500 ng/mL. If DVT was not diagnosed, patients did not receive anticoagulant treatment. Main outcome measure Symptomatic venous thromboembolism at three months. Results 1508 patients were enrolled and analysed, of whom 173 (11.5%) had DVT on scheduled diagnostic testing. Of the 1275 patients with no proximal DVT on scheduled testing who did not receive anticoagulant treatment, eight (0.6%, 95% confidence interval 0.3% to 1.2%) were found to have venous thromboembolism during follow-up. Compared with a traditional DVT testing strategy, this diagnostic approach reduced the need for ultrasonography from a mean of 1.36 scans/patient to 0.72 scans/patient (difference −0.64, 95% confidence interval −0.68 to −0.60), corresponding to a relative reduction of 47%. Conclusions The diagnostic strategy using a combination of clinical pretest probability and D-dimer identified a group of patients at low risk for DVT during follow-up while substantially reducing the need for ultrasound imaging. Registration ClinicalTrials.gov NCT02038530 .
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".