Allergen bronchoprovocation test: an important research tool supporting precision medicine
Bibliographic record
Abstract
PURPOSE OF REVIEW: Allergen bronchoprovocation test (ABT) has been used to study asthma pathophysiology and as a disease-modelling tool to assess the properties and efficacy of new asthma drugs. In view of the complexity and heterogeneity of asthma, which has driven the definition of several phenotypes and endotypes, we aim to discuss the role of ABT in the era of precision medicine and provide guidance for clinicians how to interpret and use available data to understand the implications for the benefits of asthma treatment. RECENT FINDINGS: In this review, we summarize background knowledge and applications of ABT and provide an update with recent publications on this topic. In the past years, several studies have been published on ABT in combination with non-invasive and invasive airway samplings and innovative detection techniques allowing to study several inflammatory mechanisms linked to Th2-pathway and allergen-induced pathophysiology throughout the airways. SUMMARY: ABT is a valuable research tool, which has strongly contributed to precision medicine by helping to define allergen-triggered key inflammatory pathways and airway pathophysiology, and thus helped to shape our understanding of allergen-driven asthma phenotypes and endotypes. In addition, ABT has been instrumental to assess the interactions and effects of new-targeted asthma treatments along these pathways.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".