Mucus plug reduction after endobronchial valve implantation – Results from the MMCOPD study
Bibliographic record
Abstract
Introduction: Patients with advanced COPD and pulmonary emphysema undergo endoscopic valve implantation to improve symptoms and quality of life. As a side effect, some patients report an increase in mucus production due to the implantation of foreign material. Mucus scoring on chest computed tomography (CT) is an objective method of quantifying mucus load. Aim: The prospective, single-center MMCOPD study aims to characterize the microbiome, metabolome, mucus load, symptoms (COPD assessment test, CAT) and exacerbation history of COPD patients before and after bronchoscopic interventions. Chest CT scan-based mucus scoring was performed to assess mucus load before and after endoscopic valve implantation, separately for each lung segment. Results: 50 patients (emphysema index 34 ± 10%) were analyzed with a mean total mucus score of 5.4 ± 4.9 plugs. 2.9% of all segments were unscorable due to highly diseased regions or motion artifact. 58% of all patients had ≥3 mucus plugs in total. Mucus plugs were more prevalent in lower lobes (highest score in segment LB10) than in upper lobes. For 42 patients with a follow up CT scan, mucus score decreased significantly from 5.5 ± 3.6 to 3.6 ± 4.4 plugs (p=0.002). After exclusion of all unscorable segments the results were still significant. The first two questions on cough and sputum in the CAT score did not change significantly. Conclusion: Mucus load is high in patients with pulmonary emphysema. Unexpectedly and fortunately, the mucus score on the chest CT scan decreased after endoscopic valve implantation. This finding needs further evaluation and interpretation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".