Pulmonary Embolism: Prospective Comparison of Iso-osmolar and Low-Osmolarity Nonionic Contrast Agents for Contrast Enhancement at CT Angiography
Bibliographic record
Abstract
PURPOSE: To prospectively evaluate contrast enhancement on pulmonary computed tomographic (CT) angiograms obtained by using an iso-osmolar versus a low-osmolarity contrast agent to exclude pulmonary embolism. MATERIALS AND METHODS: Written patient consent was obtained on a form approved by the institutional review board, and the board approved the study. This prospective, randomized, double-blinded clinical trial included 47 patients referred for multi-detector row CT angiography to exclude pulmonary embolism over a 5-month period. Patients received either iohexol or iodixanol as an intravenous contrast agent. Three radiologists independently evaluated enhancement homogeneity and quality in designated pulmonary artery branches at four consecutive levels in the lower lobe of the left lung from lobar to subsegmental arteries. This evaluation was performed at a workstation separately for homogeneity and quality with two different three-level scales established with consensus. Percentages of each given score were compared with the chi2 test. The mean attenuation (expressed in Hounsfield units) for each contrast agent was compared with Student t test, and interobserver agreement (kappa value) was calculated. RESULTS: The percentages of arteries graded as excellent or not diagnostic were not statistically different (P >.05), with comparison of the two contrast agents at all levels. The intensity of enhancement (quantitative evaluation of enhancement by using mean attenuation of vessel lumen) was similar (P >.05) in the two groups. The kappa values varied from 0.35 to 0.56 among readers. CONCLUSION: Use of an iso-osmolar contrast agent at multi-detector row CT angiography to exclude pulmonary embolism did not significantly improve enhancement quality when this feature was compared with that of a low-osmolarity contrast agent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".