Diagnostic Management Strategies in Patients with Suspected Deep Vein Thrombosis
Bibliographic record
Abstract
Diagnosis of deep vein thrombosis (DVT) is an important medical problem due to the high fatality rate from PE and the large number of cases not diagnosed before causing death. Over the last decade, there has been considerable research into the diagnostic process. It is widely accepted that venous ultrasound imaging is an accurate test for the diagnosis of DVT and is the imaging test of choice. Computed tomographic venography and magnetic resonance imaging are acceptable but impractical alternatives. Despite the accuracy of imaging tests, the post-test probability of disease is highly dependent on pre-test probability. Clinical evaluation tools have been developed that enable physicians to categorize accurately patients' risk prior to diagnostic imaging. One advantage of this characterization is an ability to exclude the diagnosis of DVT if the clinical probability is sufficiently low and when the D-dimer is negative. There are now a number of D-dimer assays that have well-defined specificities and sensitivities which may be used in conjunction with clinical probability. A careful combination of clinical assessment, D-dimer testing and imaging permits safe DVT rule-out protocols (even without imaging), an ability to suspect false-positive imaging results and more accurate determination of true-positive imaging. These integration strategies result in safer, more convenient and cost-effective care for patients.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".