Computerized tomographic pulmonary angiography versus ventilation perfusion lung scanning for the diagnosis of pulmonary embolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to focus on recent research that has addressed the relative merits of computed tomographic pulmonary angiography (CTPA) and ventilation perfusion (V/Q) scanning for the diagnosis of pulmonary embolism. RECENT FINDINGS: Computed tomographic pulmonary angiography is the most sensitive test for the diagnosis of pulmonary embolism and its use has been associated with a rising incidence of the condition. Diagnostic algorithms using either CTPA or V/Q scanning have proven to be comparably safe to exclude the diagnosis of pulmonary embolism. Negative multidetector CTPA study results essentially ruled out the diagnosis of pulmonary embolism without the need to routinely exclude the presence of deep vein thrombosis. Use of multidetector CTPA was associated with significant radiation exposure that potentially increases risk of secondary malignancies. This is particularly a concern for young women given the risk of breast cancer. Single photon emission tomography (SPECT) V/Q and modified diagnostic criteria for V/Q scan interpretation increased their diagnostic accuracy compared with V/Q scanning and offer nuclear medicine modalities that are alternatives to CTPA in at least some patients with suspected pulmonary embolism at a fraction of the risk of radiation exposure. Excluding low risk patients for pulmonary embolism as defined by clinical scoring systems and D-dimer testing would enhance the yield of diagnostic testing. SUMMARY: Computed tomographic pulmonary angiography is the most reliable test for diagnosis of pulmonary embolism. However, diagnostic algorithms using V/Q scanning are safe and may be preferred in some patient populations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".