Multidetector Computed Tomography for Acute Pulmonary Embolism
Bibliographic record
Abstract
BACKGROUND: The accuracy of multidetector computed tomographic angiography (CTA) for the diagnosis of acute pulmonary embolism has not been determined conclusively. METHODS: The Prospective Investigation of Pulmonary Embolism Diagnosis II trial was a prospective, multicenter investigation of the accuracy of multidetector CTA alone and combined with venous-phase imaging (CTA-CTV) for the diagnosis of acute pulmonary embolism. We used a composite reference test to confirm or rule out the diagnosis of pulmonary embolism. RESULTS: Among 824 patients with a reference diagnosis and a completed CT study, CTA was inconclusive in 51 because of poor image quality. Excluding such inconclusive studies, the sensitivity of CTA was 83 percent and the specificity was 96 percent. Positive predictive values were 96 percent with a concordantly high or low probability on clinical assessment, 92 percent with an intermediate probability on clinical assessment, and nondiagnostic if clinical probability was discordant. CTA-CTV was inconclusive in 87 of 824 patients because the image quality of either CTA or CTV was poor. The sensitivity of CTA-CTV for pulmonary embolism was 90 percent, and specificity was 95 percent. CTA-CTV was also nondiagnostic with a discordant clinical probability. CONCLUSIONS: In patients with suspected pulmonary embolism, multidetector CTA-CTV has a higher diagnostic sensitivity than does CTA alone, with similar specificity. The predictive value of either CTA or CTA-CTV is high with a concordant clinical assessment, but additional testing is necessary when the clinical probability is inconsistent with the imaging results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".