Pathogenesis-driven treatment of primary pulmonary alveolar proteinosis
Bibliographic record
Abstract
Pulmonary alveolar proteinosis (PAP) is a syndrome that results from the accumulation of lipoproteinaceous material in the alveolar space. According to the underlying pathogenetic mechanisms, three different forms have been identified, namely primary, secondary and congenital. Primary PAP is caused by disruption of granulocyte-macrophage colony-stimulating factor (GM-CSF) signalling due to the presence of neutralising autoantibodies (autoimmune PAP) or GM-CSF receptor genetic defects (hereditary PAP), which results in dysfunctional alveolar macrophages with reduced phagocytic clearance of particles, cholesterol and surfactant. The serum level of GM-CSF autoantibody is the only disease-specific biomarker of autoimmune PAP, although it does not correlate with disease severity. In PAP patients with normal serum GM-CSF autoantibody levels, elevated serum GM-CSF levels is highly suspicious for hereditary PAP. Several biomarkers have been correlated with disease severity, although they are not specific for PAP. These include lactate dehydrogenase, cytokeratin 19 fragment 21.1, carcinoembryonic antigen, neuron-specific enolase, surfactant proteins, Krebs von Lungen 6, chitinase-3-like protein 1 and monocyte chemotactic proteins. Finally, increased awareness of the disease mechanisms has led to the development of pathogenesis-based treatments, such as GM-CSF augmentation and cholesterol-targeting therapies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".