Magnitude of the late allergen response in seasonal vs perennial allergen bronchoprovocation
Bibliographic record
Abstract
Background: Allergen bronchoprovocation test (BPT) is a validated model to study asthma pathophysiology and response to treatments. The magnitude of the allergen-induced late asthmatic response (LAR) varies between individuals, possibly due to differences in immune responses, but also to the type of allergen to which the individual is sensitized. Aim: To determine the relationship between the magnitude of the LAR in mild asthmatic subjects according to the type of allergen inhaled. Methods: This is a retrospective analysis of allergen BPT data gathered between 2003 and 2010. Baseline induced sputum differential and the decrease in FEV1 at the early asthmatic response (EAR) and LAR after BPT were analyzed. Only data from subjects with a dual EAR and LAR were included. EAR was defined as a ≥20% fall in FEV1 between 0h and 3h and LAR as a ≥15% fall in FEV1 between 3h and 7h post BPT. The magnitude of the allergen response was defined as the ratio of EAR over LAR (EAR% fall in FEV1/LAR % fall in FEV1). Results: Data from 336 subjects (218F/118M, mean age±SD; 29±11 y) were analyzed: 279 were challenged with perennial allergens (HDM, cat, dog) and 57 with seasonal allergens (grass, ragweed, tree pollen). There was a significant difference between the magnitude of the responses to perennial allergens vs seasonal allergens (mean; 1.46 vs 1.69, p=0.007). There was no correlation between the magnitude of the LAR and baseline sputum eosinophil percentage in either group or in the whole sample. Conclusion: Perennial allergens induce a significantly more marked LAR for a given EAR. This may be due to a “priming” effect of persistent exposure to these allergens or to non-IgE-mediated mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".