Inhaled GM-CSF in pulmonary alveolar proteinosis (PAP) patient refractory to plasmapheresis combined with multiple whole lung lavages (WLL)
Bibliographic record
Abstract
Background: A patient with autoimmune PAP with persistent disease underwent 3 WLL, 10 plasmapheresis cycles and further 3 WLL, from October 2004 to May 2007. Despite a substantial clinical and functional benefit, the radiographic appearance showed a partial resolution of lung infiltrates(1). At the beginning of 2010, after a worsening of respiratory conditions, the patient was admitted to inhaled GM-CSF (Sagramostin) therapy as compassionate treatment. Methods: GM-CSF, dispensed byAkita 2 nebulizer (Vectura),was administered following:250 mcg/day every other week for 12 weeks, then 250 mcg/day on 2 consecutive days every 2 weeks for 6 months. Follow up visits were scheduled at 3, 10, 18, 30 months and after that once a year. Functional and HRCT data and PaO2 were collected. Results: From the start of the inhalatory therapy the patient no more required WLL. Furthermore we found a significant increase in DLCO%(p=0.013) and FVC%(p=0.023) while FEV1% show a positive trend(Fig.1). No substantial differences in blood gas analysis. The pulmonary involvement at HRCT shows a significant decrease of lung infiltrates(p=0.039) in terms of pathological segments. Conclusion: These data underscore the utility of inhaled GM-CSF not only in case of progressing disease but also in case of persistence/stabilization, in order to increase response rate. 1.Luisetti et al. Eur Respir J 2009; 33: 1220–1222.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".