Clinical Insights into Pulmonary Exacerbations in Cystic Fibrosis from the Microbiome. What Are We Missing?
Bibliographic record
Abstract
Pulmonary exacerbations account for much of the decrease in lung function and consequently most of the morbidity and mortality in patients with cystic fibrosis. These events are driven by an acute inflammatory response to infection. Recent technological advancements in molecular profiling techniques have allowed for a proliferation of microbiome studies of the lower airways of patients with cystic fibrosis. But these methods may not provide a comprehensive and unbiased measure of the lung microbiota in these patients and molecular profiles do not always translate to quantitative microbiology. Furthermore, these studies have not yet been able to provide much in the way of mechanistic insights into exacerbations or to guide patient therapy. We propose a model in which pulmonary exacerbations may be driven by an active subpopulation of the lung microbiota, which may represent only a small portion of the microbiota measured in a clinical sample. Methodology should be focused on the ultimate goal, which is to use the best available approaches to provide accurate quantitative measures of the microbiome to inform clinical decisions and provide rapid assessment of treatment efficacy. These strategies would be relevant to other chronic lung diseases such as chronic obstructive pulmonary disease and neutrophilic asthma.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".