Inflammatory subtypes in asthma: Assessment and identification using induced sputum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it