Allergen‐induced early and late asthmatic responses to inhaled seasonal and perennial allergens
Bibliographic record
Abstract
BACKGROUND: The allergen bronchoprovocation (ABP) test is a validated model to study asthma pathophysiology and response to treatments. The inhibitory effect of agents on the allergen-induced late asthmatic response (LAR) is a predictor of their efficacy in asthma treatment. However, it is difficult to predict the magnitude of a LAR, which may vary according to immune responsiveness and the type of allergen used for ABP. AIM: To determine the relationship between the magnitudes of early asthmatic response (EAR) and LAR in mild asthmatic subjects according to the type of allergen inhaled and its determinants. METHODS: This is a retrospective analysis of a large database of ABPs, all performed with a common standardized methodology. Patients were either challenged with house dust mites (HDMs), animals or pollens allergens. EAR was defined as a ≥ 20% fall in forced expiratory volume in 1 s (FEV1 ) < 3 h following ABP and LAR as a ≥ 15% fall in FEV1 between 3 and 7 h post-ABP. The ratio of EAR % fall in FEV1 /LAR % fall in FEV1 was compared between the groups of subjects according to the allergen used for ABP. RESULTS: Data from 290 subjects were analysed: 87 had an isolated EAR and 203 had a dual response (EAR + LAR). Dual responders had a significantly lower baseline PC20 , a more marked fall in FEV1 at EAR, and a trend towards higher baseline sputum eosinophil percentages. The ratio of EAR over LAR was significantly lower in HDM compared with pollen ABP, indicating a larger LAR for a similar EAR. No correlations were observed between the ratio of EAR over LAR and the various parameters recorded in the different groups analysed. CONCLUSION: Different mechanisms may be involved in modulating the magnitude of the LAR, according to the type of allergen. HDM seems to induce a stronger LAR than pollens, animal allergens being intermediary in this regard.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".