Canadian Society of Thoracic Radiology/Canadian Association of Radiologists Best Practice Guidance for Investigation of Acute Pulmonary Embolism, Part 2: Technical Issues and Interpretation Pitfalls
Bibliographic record
Abstract
The investigation of acute pulmonary embolism is a common task for radiologists in Canada. Technical image quality and reporting quality must be excellent; pulmonary embolism is a life-threatening disease that should not be missed but overdiagnosis and unnecessary treatment should be avoided. The most frequently performed imaging investigation, computed tomography pulmonary angiogram (CTPA), can be limited by poor pulmonary arterial opacification, technical artifacts and interpretative errors. Image quality can be affected by patient factors (such as body habitus, motion artifact and cardiac output), intravenous (IV) contrast protocols (including the timing, rate and volume of IV contrast administration) and common physics artifacts (including beam hardening). Mimics of acute pulmonary embolism can be seen in normal anatomic structures, disease in non-vascular structures and pulmonary artery filling defects not related to acute pulmonary emboli. Understanding these pitfalls can help mitigate error, improve diagnostic quality and optimize patient outcomes. Dual energy computed tomography holds promise to improve imaging diagnosis, particularly in clinical scenarios where routine CTPA may be problematic, including patients with impaired renal function and patients with altered cardiac anatomy.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.017 |
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".