Prediction of the rate of decline in FEV1 in smokers using quantitative computed tomography
Bibliographic record
Abstract
BACKGROUND: A study was undertaken to determine if quantitative CT estimates of lung parenchymal overinflation and airway dimensions in smokers with a normal forced expiratory volume in 1 s (FEV(1)) can predict the rapid decline in FEV(1) that leads to chronic obstructive pulmonary disease (COPD). METHODS: Study participants (n = 143; age 45-72 years; 54% male) were part of a lung cancer screening trial, had a smoking history of >30 pack years and a normal FEV(1) and FEV(1)/forced vital capacity (FVC) at baseline (mean (SD) FEV(1) 99.4 (12.8)%, range 80.2-140.7%; mean (SD) FEV(1)/FVC 77.9 (4.4), range 70.0-88.0%). An inspiratory multislice CT scan was acquired for each subject at baseline. Custom software was used to measure airway lumen and wall dimensions; the percentage of the lung inflated beyond a predicted maximal lung inflation, the low attenuation lung area with an x ray attenuation lower than -950 HU and the size distribution of the overinflated lung areas and the low attenuation area were described using a cluster analysis. Multiple regression analysis was used to test the hypothesis that these CT measurements combined with other baseline characteristics might identify those who would develop an excessive annual decline in FEV(1). RESULTS: The mean (SD) annual change in FEV(1) was -2.3 (4.7)% predicted (range -23.0% to +8.3%). Multiple regression analysis revealed that the annual change in FEV(1)%predicted was significantly associated with baseline percentage overinflated lung area measured on quantitative CT, FEV(1)% predicted, FEV(1)/FVC and gender. CONCLUSION: Quantitative CT scan evidence of overinflation of the lung predicts a rapid annual decline in FEV(1) in smokers with normal FEV(1).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".