A mini-whole lung lavage to treat autoimmune pulmonary alveolar proteinosis (PAP)
Bibliographic record
Abstract
BACKGROUND: PAP is an ultra-rare respiratory syndrome characterized by the accumulation of surfactant within the alveoli. Whole lung lavage (WLL) is the current standard of care of PAP, however it is not a standardized procedure and the total amount of fluid used to wash each lung is still debated. Considering ICU hospitalization associated risks, a "mini-WLL" with anticipated manual clapping and reduced total infusion volume and has been proposed in our center. The aim of the study is to retrospectively analyze the efficacy of mini-WLL compared to standard WLL at the Pavia center. METHODS: 13 autoimmune PAP patients eligible for WLL were included: 7 patients were admitted to mini-WLL (9 L total infusion volume for each lung) and 6 patients underwent standard WLL (14 L of infusion volume). Functional data (VC%, FVC%, TLC%, DLCO%) and alveolar-arterial gradient values (A-aO2) were collected at the baseline and 1, 3, 6, 12, 18 months after the procedure. RESULTS: A statistically significant improvement of VC% (p = 0.013, 95%CI 3.49-30.19), FVC% (p = 0.016, 95%CI 3.37-32.09), TLC% (p = 0.001, 95%CI 7.38-30.34) was observed in the mini-WLL group in comparison with the standard WLL group, while no significant difference in DLCO% and A-aO2 mean values were reported. CONCLUSION: Mini-WLL has demonstrated higher efficacy in ameliorating lung volumes, suggesting that a lower infusion volume is sufficient to remove the surfactant accumulation and possibly allows a reduced mechanical insult of the bronchi walls and the alveoli. However, no statistically significant differences were found in terms of DLCO% and Aa-O2.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".