The Diagnosis of Venous Thromboembolism
Bibliographic record
Abstract
Venous thromboembolism (VTE) is a serious and potentially fatal medical condition. Correct diagnosis and early treatment of VTE with anticoagulant drugs are critical steps in preventing further complications and recurrence. Evidence suggests that patients with suspected deep vein thrombosis (DVT) or pulmonary embolism (PE) should be managed with a diagnostic strategy that includes clinical pretest probability assessment, D-dimer test, and imaging. Clinical probability scoring, complemented by selective D-dimer testing, has become the recommended strategy for diagnosis. The reason is that overwhelming evidence suggests that patients with suspected VTE are better managed with a diagnostic strategy. If diagnostic algorithms are followed correctly, the chances of adverse events are extremely low (< 1%) in patients in whom VTE has been ruled out, whereas incomplete strategies leads to an increased risk of recurrent VTE or death. This review focuses on the application of diagnostic strategies with suspected DVT or PE into daily clinical practice while discussing the benefits and disadvantages of different approaches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".