Hereditary Pulmonary Alveolar Proteinosis: Pathogenesis, Presentation, Diagnosis, and Therapy
Bibliographic record
Abstract
RATIONALE: We identified a 6-year-old girl with pulmonary alveolar proteinosis (PAP), impaired granulocyte-macrophage colony-stimulating factor (GM-CSF) receptor function, and increased GM-CSF. OBJECTIVES: Increased serum GM-CSF may be useful to identify individuals with PAP caused by GM-CSF receptor dysfunction. METHODS: We screened 187 patients referred to us for measurement of GM-CSF autoantibodies to diagnose autoimmune PAP. Five were children with PAP and increased serum GM-CSF but without GM-CSF autoantibodies or any disease causing secondary PAP; all were studied with family members, subsequently identified patients, and controls. MEASUREMENT AND MAIN RESULTS: Eight children (seven female, one male) were identified with PAP caused by recessive CSF2RA mutations. Six presented with progressive dyspnea of insidious onset at 4.8 ± 1.6 years and two were asymptomatic at ages 5 and 8 years. Radiologic and histopathologic manifestations were similar to those of autoimmune PAP. Molecular analysis demonstrated that GM-CSF signaling was absent in six and severely reduced in two patients. The GM-CSF receptor β chain was detected in all patients, whereas the α chain was absent in six and abnormal in two, paralleling the GM-CSF signaling defects. Genetic analysis revealed multiple distinct CSF2RA abnormalities, including missense, duplication, frameshift, and nonsense mutations; exon and gene deletion; and cryptic alternative splicing. All symptomatic patients responded well to whole-lung lavage therapy. CONCLUSIONS: CSF2RA mutations cause a genetic form of PAP presenting as insidious, progressive dyspnea in children that can be diagnosed by a combination of characteristic radiologic findings and blood tests and treated successfully by whole-lung lavage.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".