Pulmonary MRI morphometry modeling of airspace enlargement in chronic obstructive pulmonary disease and alpha‐1 antitrypsin deficiency
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Bibliographic record
Abstract
Purpose We generated lung morphometry measurements using single‐breath diffusion‐weighted MRI and three different acinar duct models in healthy participants and patients with emphysema stemming from chronic obstructive lung disease (COPD) and alpha‐1 antitrypsin deficiency (AATD). Methods Single‐breath‐inhaled 3 He MRI with five diffusion sensitizations (b‐value = 0, 1.6, 3.2, 4.8, and 6.4 s/cm 2 ) was used, and signal intensities were fit using a cylindrical and single‐compartment acinar‐duct model to estimate MRI‐derived mean linear intercept ( L m ) and surface‐to‐volume ratio ( S/V ). A stretched exponential model was also developed to estimate the mean airway length and L m . Results We evaluated 42 participants, including 15 elderly never‐smokers (69 ± 5 years), 12 ex‐smokers without COPD (67 ± 11 years), 9 COPD ex‐smokers (80 ± 6 years), and 6 AATD patients (59 ± 6 years). In the never‐ and ex‐smokers, the diffusing capacity of the lung for carbon monoxide (DL CO ) and computed tomography relative area of less than − 950 Hounsfield units (RA 950 ) were normal, but these were abnormal in the COPD and AATD patients, which is reflective of emphysema. Although cylindrical and stretched‐exponential‐model estimates of L m and S/V were not significantly different, the single‐compartment‐model estimates were significantly different ( P < 0.05) for the never‐ and ex‐smoker subgroups. All models estimated significantly worse L m and S/V in the AATD and COPD subgroups compared with the never‐ and ex‐smokers without emphysema. Conclusions Differences in airspace enlargement may be estimated using L m and S / V , generated using MRI and a stretched‐exponential or cylindrical model of the acinar ducts. Magn Reson Med 79:439–448, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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