D-Dimer as a Screening Tool for Ultrasound Examination for Diagnosis of Deep Vein Thrombosis
Bibliographic record
Abstract
Ultrasound (US) examinations have become the standard for evaluation of deep venous thrombosis (DVT). Evaluation for a possible DVT constitutes more than 40% of the daily ultrasound examinations performed at St. John's Regional Health Center. Of the US examinations performed for DVT, 80% are negative. The addition of D-dimer testing to the diagnostic algorithm has the potential to make the diagnosis of DVT in outpatients more convenient and economical. Ultrasound examination and interpretation is expensive. This project was to evaluate the D-dimer determination as a screening tool to indicate the need for US evaluation for DVT. The focus of this evaluation was the negativity of the D-dimer as a predictor of a normal venous examination. This study found that with a negative D-dimer result, more than 90% of the venous examinations were negative for DVT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".