Diffuse and interstitial lung disease and childhood rheumatologic disorders
Bibliographic record
Abstract
PURPOSE OF REVIEW: Advances in genetics and clinical diagnostics, along with recently described clinical entities and refined classification schemes, have improved our understanding of diffuse and interstitial lung diseases in children. This review presents recent updates in these disorders in the context of systemic inflammatory conditions. RECENT FINDINGS: Classification of childhood diffuse lung disease (DLD) using adult paradigms is not useful. Distinct clinical-pathologic entities exist in children. Infants are more likely to present with genetic and developmental disorders, and older children with inflammatory and immune-mediated conditions. A combination of clinical evaluation, high-resolution computed tomography scanning, pulmonary function testing and serology, with bronchoscopy and surgical lung biopsy in selected cases, is most useful in the evaluation of DLD in the context of rheumatologic conditions. Common causes of DLD, such as infection, especially in the setting of immunodeficiency, must be ruled out. Optimal therapy for specific disorders will require careful analysis of data from national registries. Emerging use of biomarkers and high-throughput molecular analysis will yield novel insight into these disorders. SUMMARY: In the setting of known or suspected rheumatologic disorders, diagnosis and management of DLD are challenging, and require close collaboration among rheumatologists, pulmonologists, and other specialists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".