Assessing pulmonary exacerbations (PEx) in people with cystic fibrosis (pwCF) with dynamic functional lung MRI
Bibliographic record
Abstract
There are two types of functional methodology to assess ventilation with lung MRI:(i)observing ventilatory mechanics during the respiratory cycle as an indirect measure (α-mapping/Fourier decomposition);(ii)observing contrast gas distribution after inhalation; our group uses dynamic oxygen enhanced (OE-)MRI as a feasible alternative to hyperpolarised gas MRI. We hypothesized that OE-MRI would be more sensitive to detect PEx in pwCF than α-mapping. PwCF (n=14) performed spirometry, α-mapping & OE-MRI on the same day. PEx were pragmatically defined as a new course of anti-biotics/fungals to treat worsening symptoms of lung disease and/or unexpected drop in ppFEV1. α-mapping parameters: ventilation defect percentage (α-VDP). OE-MRI parameters: OE-VDP and ∆R2* (ventilation signal). Data are median(range) and compared with Wilcoxon signed matched pairs. Baseline values: OE-VDP 22.6%(6.9-52.0%), ∆R2* 0.062(0.03-0.10), α-VDP 9.7% (1.0-39.3%) and ppFEV1 88%(71-105%). PEx values were significantly worse for OE-VDP 32.3%(5.4-57.0%, P<0.001), ∆R2* 0.05 (0.02-0.09, P<0.001) ppFEV1 85% (65-102%, P<0.05) but not for α-VDP 13.9%(1.1-36.4%, P>0.05). α-VDP was significantly lower than OE-VDP at both visits (P<0.05 and P<0.001 respectively). This sub-study suggests that OE-MRI and α-mapping measure different aspects of respiratory physiology. Assessing longitudinal disease status and detecting accute changes may require a combined approach. This ongoing project will assess repeatability, disease progression, responsiveness, and assess spatial heterogeneity. We aim ultimately to establish the utility of OE-MRI as a clinical and research outcome for the future. Funded by the CF Foundation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".