Evaluation of D-Dimer in the Diagnosis of Suspected Deep-Vein Thrombosis
Bibliographic record
Abstract
BACKGROUND: Several diagnostic strategies using ultrasound imaging, measurement of D-dimer, and assessment of clinical probability of disease have proved safe in patients with suspected deep-vein thrombosis, but they have not been compared in randomized trials. METHODS: Outpatients presenting with suspected lower-extremity deep-vein thrombosis were potentially eligible. Using a clinical model, physicians evaluated the patients and categorized them as likely or unlikely to have deep-vein thrombosis. The patients were then randomly assigned to undergo ultrasound imaging alone (control group) or to undergo D-dimer testing (D-dimer group) followed by ultrasound imaging unless the D-dimer test was negative and the patient was considered clinically unlikely to have deep-vein thrombosis, in which case ultrasound imaging was not performed. RESULTS: Five hundred thirty patients were randomly assigned to the control group, and 566 to the D-dimer group. The overall prevalence of deep-vein thrombosis or pulmonary embolism was 15.7 percent. Among patients for whom deep-vein thrombosis had been ruled out by the initial diagnostic strategy, there were two confirmed venous thromboembolic events in the D-dimer group (0.4 percent; 95 percent confidence interval, 0.05 to 1.5 percent) and six events in the control group (1.4 percent; 95 percent confidence interval, 0.5 to 2.9 percent; P=0.16) during three months of follow-up. The use of D-dimer testing resulted in a significant reduction in the use of ultrasonography, from a mean of 1.34 tests per patient in the control group to 0.78 in the D-dimer group (P=0.008). Two hundred eighteen patients (39 percent) in the D-dimer group did not require ultrasound imaging. CONCLUSIONS: Deep-vein thrombosis can be ruled out in a patient who is judged clinically unlikely to have deep-vein thrombosis and who has a negative D-dimer test. Ultrasound testing can be safely omitted in such patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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, 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".