Pulmonary hamartomas: <scp>CT</scp> pixel analysis for fat attenuation using radiologic–pathologic correlation
Bibliographic record
Abstract
INTRODUCTION: To assess the accuracy of CT pixel analysis for fat attenuation in pulmonary hamartomas. METHODS: Retrospective review identified 32 patients in three separate groups; pathologically proven hamartoma (n = 11), hamartoma diagnosed on imaging (n = 9) and a control group (n = 14) of pathology-proven non-hamartomatous smoothly marginated solitary pulmonary nodules. All lesions were assessed using: visual assessment for fat, pixel analysis of the inner 2/3rds and mean attenuation of the entire lesion, using an internal reference for fat. Fat percentages on CT and at histology were compared. RESULTS: Visual assessment for macroscopic fat was the most reliable method for diagnosing pulmonary hamartoma. Combining percentage of fat-attenuation pixels in the inner 2/3rds of the lesion improved specificity to 100%. Mean attenuation or pixel analysis in isolation were not helpful in lesional characterization. CONCLUSION: Combining percentage fat-attenuating pixels in the inner 2/3rds with visual assessment for macroscopic fat improves specificity for diagnosing pulmonary hamartomas.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".