Diagnosis of Subsegmental Pulmonary Emboli
Bibliographic record
Abstract
We measured sensitivity, positive predictive value, and free-response receiver operating characteristic (FROC) of 20 radiologists detecting subsegmental-sized pulmonary emboli in a porcine model using either contrast-enhanced computed tomography (CT) or digital subtraction (DS) pulmonary angiography. Colored methacrylate beads (4.2 and 3.8 mm diameter) were injected into 9 anesthetized juvenile pigs. CT and DS pulmonary angiography images were obtained before and after a pulmonary infiltrate was introduced into the lower lobes. Following imaging, the pigs were euthanized, and the pulmonary arterial tree was cast using clear methacrylate allowing direct visualization of emboli. The 20 radiologists used a custom-made computer application to display the images on their personal computer and record their diagnoses. The results were mailed electronically to the coordinating center for comparison with the cast of the pulmonary vasculature. Twenty-three emboli were included in the statistical analysis. Overall sensitivity for spiral CT and angiography, respectively, was: 60 +/- 18% and 72 +/- 11% (P = 0.06). Positive predictive value for spiral CT and angiography, respectively, was: 49 +/- 24% and 58 +/- 23% (P = 0.25). There was a large variation in both sensitivity and positive predicted values between Readers. There was no difference in sensitivity or positive predictive value between radiologists from community or academic centers (P > 0.27). FROC analysis showed no significant difference between CT or DS (P = 0.27). In conclusion, in this porcine model, there is no overall diagnostic advantage to using DS pulmonary angiography rather than contrast-enhanced spiral CT for the diagnosis of PE when images are interpreted by radiologists located in either academic or community hospital settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".