Qualitative and Quantitative Assessment of Smoking-related Lung Disease
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: The purpose of this research is to examine the role that differing levels of adaptive statistical iterative reconstruction (ASIR) have on the qualitative and quantitative assessment of smoking-related lung disease. MATERIALS AND METHODS: Institutional board review approval was obtained. A total of 52 patients undergoing clinically indicated low-dose computed tomographic (CT) examinations of the chest (100 kVp, 65 mAs, mean radiation dose 1.0±0.12 mSv), with reconstruction of data with different levels of blended ASIR (0%, 40%, and 100%), were consented. Qualitative assessment of CT data sets was performed by 2 trained thoracic radiologists blinded to clinical history, spirometry, and quantitative data for the presence of emphysema (%/lung zone) and the degree of respiratory bronchiolitis. Quantitative analysis was performed (Apollo Image analysis, VIDA Diagnostics) to assess emphysema and airway measures of chronic obstructive pulmonary disease. RESULTS: The application of ASIR results in alterations in both qualitative and quantitative assessment of smoking-related lung disease. As levels of ASIR increased, both readers scored more respiratory bronchiolitis (P<0.05). At increased levels of ASIR (ie, 100% vs. 0%), the amount of emphysema measured (% below -950 HU) decreased, the number of airways measured diminished, and the airway thickness (Pi10mm) increased (P<0.001). CONCLUSIONS: The use of ASIR alters both the qualitative and quantitative assessment of smoking-related lung disease. Although a powerful tool to allow dose reduction, caution must be exercised when iterative reconstruction techniques are utilized when evaluating CT examinations for findings of chronic obstructive pulmonary disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it