Qualitative and Quantitative Assessment of Smoking-related Lung Disease
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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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".