Pulmonary MRI morphometry modeling of airspace enlargement in chronic obstructive pulmonary disease and alpha‐1 antitrypsin deficiency
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 3He MRI with five diffusion sensitizations (b‐value = 0, 1.6, 3.2, 4.8, and 6.4 s/cm2) was used, and signal intensities were fit using a cylindrical and single‐compartment acinar‐duct model to estimate MRI‐derived mean linear intercept (Lm) and surface‐to‐volume ratio (S/V). A stretched exponential model was also developed to estimate the mean airway length and Lm. 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 (DLCO) and computed tomography relative area of less than −950 Hounsfield units (RA950) 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 Lm 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 Lm 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 Lm 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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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".