Strategic plan for pediatric respiratory diseases research: An NHLBI working group report
Bibliographic record
Abstract
The Division of Lung Diseases of the National Heart, Lung and Blood Institute (NHLBI) recently held a workshop to identify gaps in our understanding and treatment of childhood lung diseases and to define strategies to enhance translational research in this field. Leading experts with diverse experience in both laboratory and patient-oriented research reviewed selected areas of pediatric lung diseases, including perinatal programming and epigenetic influences; mechanisms of lung injury, repair, and regeneration; pulmonary vascular disease (PVD); sleep and control of breathing; and the application of novel translational methods to enhance personalized medicine. This report summarizes the proceedings of this workshop and provides recommendations for emphasis on targeted areas for future investigation. The priority areas identified for research in pediatric pulmonary diseases included: (1) epigenetic and environmental influences on lung development that program pediatric lung diseases, (2) injury, regeneration, and repair in the developing lung, (3) PVD in children, (4) development and adaptation of ventilatory responses to postnatal life, (5) nonatopic wheezing: aberrant large airway development or injury? (6) strategies to improve assessment, diagnosis, and treatment of pediatric respiratory diseases, and (7) predictive and personalized medicine for children.
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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 | 0.055 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".