Venous thromboembolism: diagnosis and management of pulmonary embolism
Bibliographic record
Abstract
Pulmonary embolism (PE) affects 0.5-1 per 1000 people in the general population each year, and is one of the most common preventable causes of death among hospitalised patients. The clinical diagnosis of PE is unreliable and must be confirmed objectively with ventilation perfusion scanning or computed tomography pulmonary angiography. The diagnosis of PE can be reliably excluded, without the need for diagnostic imaging, if the clinical pretest probability for PE is low and the D-dimer assay result is negative. The initial treatment of PE is low-molecular-weight heparin or unfractionated heparin for at least 5 days, followed by warfarin (target international normalised ratio [INR], 2.0-3.0) for at least 3-6 months. Patients with a high clinical pretest probability of PE should commence treatment immediately while awaiting the results of the diagnostic work-up. Thrombolysis is indicated for patients with objectively confirmed PE who are haemodynamically unstable. Percutaneous transcatheter or surgical embolectomy may be life-saving in patients ineligible for, or unresponsive to, thrombolytic therapy. Unresolved issues in the management of venous thromboembolism include the roles of thrombophilia testing, thrombolysis for the treatment of stable PE patients who present with right ventricular dysfunction, and new anticoagulants; and the duration of anticoagulation for first unprovoked venous thromboembolism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".