Inflammatory subtypes in asthma: Assessment and identification using induced sputum
Bibliographic record
Abstract
OBJECTIVE: The authors sought to investigate the detection of non-eosinophilic asthma using induced sputum. Although this is an important subtype of clinical asthma, its recognition is not standardized. METHODS: Adult non-smokers with asthma and healthy controls underwent sputum induction and hypertonic saline challenge. Non-eosinophilic asthma was defined as symptomatic asthma with normal sputum eosinophil counts. The normal range for sputum eosinophil count was determined using the 95th percentile from the healthy control group as a cut-off point. RESULTS: The recognition of non-eosinophilic asthma using eosinophil proportion was in agreement with a definition based on absolute eosinophil count (kappa 0.67). Non-eosinophilic asthma was a stable subtype over both the short term (4 weeks) and longer term (5 years, kappa 0.77). Airway inflammation in asthma could be categorized into four inflammatory subtypes based on sputum eosinophil and neutrophil proportions. These subtypes were neutrophilic asthma, eosinophilic asthma, mixed granulocytic asthma and paucigranulocytic asthma. Subjects with increased neutrophils (neutrophilic asthma and mixed granulocytic asthma) were older and had an increased total cell count and cell viability compared with other subtypes. CONCLUSION: Induced sputum eosinophil proportion is a good discriminator for eosinophilic asthma, providing a reproducible definition of a homogenous group. The remaining non-eosinophilic subjects are heterogeneous and can be further classified based on the presence of neutrophils. These inflammatory subtypes have important implications for the investigation and characterization of airway inflammation in 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".