Quantitative computed tomography and visual emphysema scores: association with lung function decline
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Bibliographic record
Abstract
Background Computed tomography (CT) visual emphysema score is a better predictor of mortality than single quantitative CT emphysema measurements in COPD, but there are numerous CT measurements that reflect COPD-related disease features. The purpose of this study was to determine if linear combinations of quantitative CT measurements by principal component analysis (PCA) have a greater association with forced expiratory volume in 1 s (FEV 1 ) lower limit of normal (LLN) annualised change (ΔFEV 1 ) than visual emphysema score in COPD. Methods In this retrospective, longitudinal study, demographic, spirometry and CT images were acquired. CT visual emphysema score and quantitative analysis were performed; low attenuation area <950 HU (LAA 950 ) and 12 other quantitative CT measurements were investigated. PCA was used for CT feature extraction. Multiple linear regression models for baseline FEV 1 LLN and 6-year ΔFEV 1 were used to determine associations with visual emphysema score and CT measurements. A total of 725 participants were analysed (n=299 never-smokers, n=242 at-risk and n=184 COPD). Results Quantitative CT measures (LAA 950 and PCA components) were independently statistically significant (p<0.05) in predicting baseline FEV 1 LLN, whereas visual emphysema score was not statistically significant in any baseline model. When predicting 6-year ΔFEV 1 , only visual emphysema score was significant (p<0.05) in models with LAA 950 and PCA combination of emphysema measurements. In the model with PCA using all CT measurements predicting 6-year ΔFEV 1 , visual emphysema score (p=0.021) along with one PCA component (p=0.004) were statistically significant. Conclusions PCA with a combination of CT measurements reflecting several different COPD-related disease features independently predicted baseline lung function and increased the relative importance of quantitative CT compared with visual emphysema score for predicting lung function decline.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it