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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".