Application of the defect distribution index to functional lung MRI of pediatric cystic fibrosis lung disease and controls
Bibliographic record
Abstract
Introduction Functional magnetic resonance imaging (MRI) of the lung usually assesses lung impairment as ventilation defect percentage (VDP). However, VDP only reflects the overall burden of disease and does not characterize the regional distribution (i.e. pattern) of defects. The defect distribution index (DDI) is a metric which shows quantitatively how clustered versus scattered defects are with a higher DDI indicating more clustered defects. Aim To assess the applicability and validity of the DDI to 129 Xe-MRI and PREFUL-MRI of CF lung disease. Methods The DDI algorithm was applied to fractional ventilation maps previously acquired with 129 Xe-MRI and PREFUL-MRI of 37 children with CF and 13 healthy controls. Results The calculation of DDI was feasible for all MRI data. DDI was significantly higher in patients with CF compared to healthy controls (mean difference [95 % CI] 129 Xe-MRI DDI 60 %mean -1.94 [-2.86 – -1.02], p=0.0001), strongly correlated with other functional outcomes such as VDP and the lung clearance index, and decreased significantly in CF patients with pulmonary exacerbations after antibiotic treatment (e.g. 129 Xe-MRI DDI 60 % mean –1.03 [-0.44 – -1.63], p=0.002). Conclusion The DDI is applicable to functional 129 Xe-MRI and PREFUL-MRI data providing complementary information to VDP by assessing defect distribution rather than defect size. It shows meaningful clinimetric properties and improves with treatment. The DDI shows potential as a parameter for comprehensive monitoring of CF lung disease and treatment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".