Chronic Obstructive Pulmonary Disease: Longitudinal Hyperpolarized <sup>3</sup> He MR Imaging
Bibliographic record
Abstract
PURPOSE: To quantitatively evaluate a small pilot group of ex-smokers with chronic obstructive pulmonary disease (COPD) and healthy volunteers during approximately 2 years by using hyperpolarized helium 3 ((3)He) magnetic resonance (MR) imaging. MATERIALS AND METHODS: All subjects provided written informed consent to the study protocol, which was approved by the local research ethics board and Health Canada and was compliant with the Personal Information Protection and Electronic Documents Act and HIPAA. Hyperpolarized (3)He MR imaging, hydrogen 1 MR imaging, spirometry, and plethysmography were performed in 15 ex-smokers with COPD and five healthy volunteers (with the same mean age and age range) at baseline and 26 months +/- 2 (standard deviation) later. Apparent diffusion coefficients (ADCs) derived from (3)He MR imaging were calculated from diffusion-weighted (3)He MR images, and (3)He ventilation defect volume (VDV) and ventilation defect percentage (VDP) were generated after manual segmentation of (3)He MR spin-density images. RESULTS: For subjects with COPD, significant increases in (3)He MR imaging-derived VDV (P = .03), VDP (P = .006), and ADC (P = .02) were detected, whereas there was no significant change in forced expiratory volume in 1 second (FEV(1)) (P = .97). For healthy never-smokers, there was no significant change in imaging or pulmonary function measurements at follow-up. There was a significant correlation between changes in FEV(1) and changes in VDV (r = -0.70, P = .02) and VDP (r = -0.70, P = .03). CONCLUSION: For this small pilot group of ex-smokers with COPD, (3)He MR imaging-derived VDV, VDP, and ADC measurements worsened significantly, but there was no significant change in FEV(1), suggesting increased sensitivity of hyperpolarized (3)He MR imaging for depicting COPD changes during short time periods.
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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 | 0.001 | 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 | 0.001 | 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".