Upper-lobe CT imaging features improve prediction of lung function decline in COPD
Bibliographic record
Abstract
Background: It is unknown whether prediction models for lung function decline using computed tomography (CT) imaging-derived features from the upper lobes improve performance compared with globally derived features in individuals at risk of and with COPD. Methods: Individuals at risk (current or former smokers) and those with COPD from the Canadian Cohort Obstructive Lung Disease (CanCOLD) retrospective study, were investigated. A total of 103 CT features were extracted globally and regionally, and were used with 12 clinical features (demographics, questionnaires and spirometry) to predict rapid lung function decline for individuals at risk and those with COPD. Machine-learning models were evaluated in a hold-out test set using the area under the receiver operating characteristic curve (AUC) with DeLong's test for comparison. Results: A total of 780 participants were included (n=276 at risk; n=298 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1 COPD; n=206 GOLD 2+ COPD). For predicting rapid lung function decline in those at risk, the upper-lobe CT model obtained a significantly higher AUC (AUC=0.80) than the lower-lobe CT model (AUC=0.63) and global model (AUC=0.66; p<0.05). For predicting rapid lung function decline in COPD, there was no significant differences between the upper-lobe (AUC=0.63), lower-lobe (AUC=0.59) or global CT features model (AUC=059; p>0.05). Conclusion: CT features extracted from the upper lobes obtained significantly improved prediction performance compared with globally extracted features for rapid lung function decline in early/mild COPD.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".