Detection of Lung Perfusion Abnormalities Using Computed Tomography in a Porcine Model of Pulmonary Embolism
Bibliographic record
Abstract
The purpose of this study was to identify perfusion defects of the lung using computed tomography (CT). A balloon catheter was placed in a lobar pulmonary artery of six anesthetized, ventilated, juvenile pigs to simulate occlusive segmental embolus. Contrast medium was injected via a central venous catheter at rates of 1.5, 3, 4.5, and 9 ml/s in each pig. A 40-second single-level cine CT was acquired distal to the inflated balloon during suspended inspiration. Three computer-manipulated images (time to maximal enhancement, change in maximal attenuation, maximal contrast minus precontrast subtraction) were generated using custom software and compared with the unmodified maximal enhancement and precontrast images. Two independent observers identified perfusion defects and scored the level of confidence (5-point scale) on all five images. Regions of interest were drawn in perfused and nonperfused lung and time-attenuation curves were generated. Perfusion defects were accurately (99.8 +/- 0.3%) and confidently (4.5 +/- 0.6) detected and there was excellent interobserver agreement (Kappa 0.99 +/- 0.02) on all computer-manipulated images. There was a significant increase in confidence (p < 0.05) between contrast medium injection rates of 1.5 and 9 ml/s. A linear relationship exists (r = 0.88) between injection rate and change in maximal attenuation. In conclusion, perfusion defects of the lung are seen using computer-manipulated CT images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".