Management of Suspected Deep Venous Thrombosis in Outpatients by Using Clinical Assessment and <scp>d</scp>-dimer Testing
Bibliographic record
Abstract
BACKGROUND: When deep venous thrombosis is suspected, objective testing is required to confirm or refute the diagnosis. OBJECTIVE: To determine whether the combination of a low clinical suspicion and a normal D -dimer result rules out deep venous thrombosis. DESIGN: Prospective cohort study. SETTING: Three tertiary care hospitals in Canada. PATIENTS: 445 outpatients with a suspected first episode of deep venous thrombosis. INTERVENTIONS: Patients were categorized as having low, moderate, or high pretest probability of thrombosis and underwent whole-blood D -dimer testing. Patients with a low pretest probability and a negative result on the D -dimer test had no further diagnostic testing and received no anticoagulant therapy. Additional diagnostic testing was done in all other patients. MEASUREMENTS: Venous thromboembolic events during 3-month follow-up. RESULTS: 177 (40%) patients had both a low pretest probability and a negative D -dimer result. One of these patients had deep venous thrombosis during follow-up (negative predictive value, 99.4% [95% CI, 96.9% to 100%]). CONCLUSION: The combination of a low pretest probability of deep venous thrombosis and a negative result on a whole-blood D -dimer test rules out deep venous thrombosis in a large proportion of symptomatic outpatients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".