Bibliographic record
Abstract
In this issue of Respirology, Montella and colleagues ask this question: How does high‐field chest MRI compare with CT of children with non‐cystic fibrosis (CF) lung disease? In an important extension of the first description of this study where they compared MRI and CT with pulmonary function measurements, the authors evaluated how widely‐used chest CT and almost never‐utilized lung MRI compare for diagnostic imaging of chronic lung disease. Here they show that high‐field (3Tesla as compared with the 1.5Tesla clinical standard) thoracic MRI has high reliability and good‐to‐excellent agreement with CT, definitively answering the important question at hand; their results support more widespread and routine use of MRI in longitudinal monitoring of chronic lung disease, especially in children as well as further optimization and improvement of lung MRI methods. Importantly, non‐CF lung disease accounts for the majority of paediatric pulmonary abnormalities and the increasing prevalence and economic burden related to chronic respiratory disease should motivate the research and development of novel MRI methods for serial and longitudinal imaging.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".