Validation of the Canadian Clinical Probability Model for Acute Venous Thrombosis
Bibliographic record
Abstract
Objective: To validate the predictive value of the Canadian clinical probability model for acute venous thrombosis, which, to the best of the authors' knowledge, has not been done in emergency department (ED) settings outside of Canada. Methods: Demographic and clinical information, rapid D-dimer testing, and venous ultrasound imaging were obtained among patients presenting with clinically suspected venous thrombosis at a university-affiliated ED. A diagnosis of deep venous thrombosis (DVT) was made based on venous ultrasound test results or objectively documented venous thromboembolism during a 12-week follow-up period. The probability of venous thrombosis was calculated using the Canadian clinical probability model. Results: Among 102 patients, 17 (17%) were diagnosed as having venous thrombosis initially or during the three-month follow-up period. The frequency of venous thrombosis among patients categorized as having high probability was 10 of 17 [59%, 95% confidence interval (95% CI) = 35% to 82%], 6 of 44 (14%, 95% CI = 4% to 24%) with intermediate probability, and 1 of 41 (2%, 95% CI = 0.1% to 11%) with low probability. This compares with respective values of 49%, 14%, and 3%, reported by Canadian researchers in an ED study. Forty-one of 102 (40%) patients had an alternate diagnosis as likely or more likely than venous thrombosis, but only three (7%, 95% CI = 2% to 18%) of these had venous thrombosis. Conclusions: Use of the Canadian probability model for DVT in this ED resulted in effective risk stratification, comparable to previously published results.
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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 | 0.013 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".