Prediction of the rate of decline in FEV1 in smokers using quantitative computed tomography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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