Exploring the Cystic Fibrosis Lung Microbiome: Making the Most of a Sticky Situation
Bibliographic record
Abstract
Chronic lower respiratory tract infections are a leading contributor to morbidity and mortality in persons with cystic fibrosis (pwCF). Traditional respiratory tract surveillance culturing has focused on a limited range of classic pathogens; however, comprehensive culture and culture-independent molecular approaches have demonstrated complex communities highly unique to each individual. Microbial community structure evolves through the lifetime of pwCF and is associated with baseline disease state and rates of disease progression including occurrence of pulmonary exacerbations. While molecular analysis of the airway microbiome has provided insight into these dynamics, challenges remain including discerning not only "who is there" but "what they are doing" in relation to disease progression. Moreover, the microbiome can be leveraged as a multi-modal biomarker for both disease activity and prognostication. In this article, we review our evolving understanding of the role these communities play in pwCF and identify challenges in translating microbiome data to clinical practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".