Evaluating bronchodilator effects in chronic obstructive pulmonary disease using diffusion-weighted hyperpolarized helium-3 magnetic resonance imaging
Bibliographic record
Abstract
The objective of this study was to evaluate the regional effects of bronchodilator administration in chronic obstructive pulmonary disease (COPD) using hyperpolarized helium-3 ((3)He) MRI apparent diffusion coefficient (ADC). Ten COPD ex-smokers provided written, informed consent and underwent diffusion-weighted, hyperpolarized (3)He MRI, spirometry, and plethysmography before and 25 ± 2 min after bronchodilator administration. Pre- and postsalbutamol whole-lung (WL) ADC maps were generated and registered together to identify the lung regions containing the (3)He signal at both time points, and mean ADC within those regions of interest (ROI) was determined for a measurement of previously ventilated ROI ADC (ADC(P)). Lung ROI with (3)He signal at both time points was used as a binary mask on postsalbutamol WL ADC maps to obtain an ADC measurement for newly ventilated ROI (ADC(N)). Postsalbutamol, no significant differences were detected in WL ADC (P = 0.516). There were no significant differences between ADC(N) and ADC(P) postsalbutamol (P = 1.00), suggesting that the ADC(N) lung regions were not more emphysematous than the lung ROI participating in ventilation before bronchodilator administration. Postsalbutamol, a statistically significant decrease in ADC(P) (P = 0.01) was detected, and there were significant differences between ADC(P) in the most anterior and most posterior image slices (P = 0.02), suggesting a reduction in regional gas trapping following bronchodilator administration. Regional evaluation of tissue microstructure using hyperpolarized (3)He MRI ADC provides insights into lung alterations that accompany improvements in regional (3)He gas distribution after bronchodilator administration.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".