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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".