Don’t Forget the Kids!: Novel Pulmonary MRI and AI of Neonatal Lung Disease
Bibliographic record
Abstract
N eonatal pulmonary disease, including bronchopulmo- nary dysplasia (BPD), is the final frontier for modern medical imaging.Neonatal lungs are tiny and, in cases that require hospital-based care, usually they are not fully developed.Neonates themselves cannot respond to instructions or lie still easily; those who are acutely ill need critical care support.The structure and function of the respiratory system at birth and at hospital discharge play an enormous role in the long-term achievement of optimal lung structure and function in growing children as they approach adulthood (1).In other words, the impact of neonatal care can last a lifetime.Unfortunately, the diagnosis of lung abnormalities in premature and full-term neonates still relies mainly on clinical signs and observations, which typically are managed in the intensive care unit with the aid of chest radiography.Repeat chest CT generally is not performed because of the risk of ionizing radiation to patients who are so early in life.As such, it remains difficult to provide a framework to understand potential long-term outcomes and prognoses so that the parents of neonatal patients can plan for expected hurdles and aftereffects.Into this clinical care gulf has emerged a cadre of pioneering researchers and clinicians dedicated to improving neonatal imaging using the radiation-free methods of US (2) and MRI (3).They bring the hope that if we discover Don't Forget the Kids!: Novel Pulmonary MRI and AI of Neonatal Lung Disease
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".