A new naturalistic asthma model in the environmental exposure chamber to study mild asthmatics safely and consistently
Bibliographic record
Abstract
Background : A low dose naturalistic allergen challenge model to study mild asthmatics would aid in the development of effective drug therapies. We investigated both model specificity and SPT or IgE classification as a predictor of change in lung function and lower airway symptoms in asthmatics. Methods : A meta-analysis was conducted on 323 subjects (53 mild asthmatics) with a history of cat allergy and positive SPT to cat allergen. Subjects were exposed to airborne cat allergen in a naturalistic Environmental Exposure Chamber (EEC) model for 3 hrs over 4 consecutive days. FEV 1 was assessed before and after EEC and asthma symptom scores were recorded every 30 mins in the EEC. Results : Low dose airborne naturalistic challenge resulted in: 1) A consistent drop in FEV 1 over 4 days allergen challenge. Asthmatics have a greater reduction in FEV 1 (L) over all days (p≤0.001). 2) Increase in SPT moderately predicts a larger decline in lung function. 3) Increase in IgE class (C) predicts a larger drop in lung function (ΔFEV 1 (L)) with allergen exposure: IgE ≥ C1 = 0.56 ± 0.06L vs. 0.31 ± 0.02L (p≤0.001); IgE ≥ C2= 0.59 ± 0.06L vs 0.34 ± 0.02L (p≤0.001); and IgE ≥ C3=0.66 ± 0.08L vs 0.44 ± 0.03L (p=0.01) in asthmatics compared to non-asthmatics, respectively. 4) Asthma symptom scores were higher in asthmatics than non-asthmatics (Mean Score 3.23 ± 0.30 vs. 2.60 ± 0.12 (p=0.05)). 5) Neither SPT or IgE class is predictive of asthma symptoms with allergen exposure. Conclusions : The EEC Asthma Model represents a more naturalistic model for the study of asthma. IgE and SPT are useful to characterize asthmatic patients and safely predict their response in the EEC.
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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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".