The Use of Probiotics in Respiratory Allergy
Bibliographic record
Abstract
There is a high and steadily increasing prevalence of respiratory allergy throughout the world, especially in paediatric population and in industrialized and developing countries. A complex interplay between genetic and environmental factors has been implicated to explain this dramatic increase in prevalence of allergic diseases. It has been suggested that exposure to microbes plays a critical role in the development of the early immune system and may contribute to allergic diseases through their effect on mucosal immunity. Probiotics, microorganisms exerting beneficial effects on the host, are used in a great number of paediatric and adult diseases, mainly gastrointestinal disorders, but they have been proposed to be beneficial also in allergic diseases. Different trials have been published finding benefits in the use of probiotics in prevention and treatment of atopic dermatitis, but to date, studies have yielded inconsistent findings to support a protective association between their use on prevention of allergic rhinitis or asthma. However, probiotics may be beneficial in improving symptoms and quality of life in patients with allergic rhinitis although it remains limited due to study heterogeneity and variable outcome measures. As a result of these controversies, future investigations with a better standardization are needed. In this review, we summarize recent clinical research to elucidate the mechanisms of probiotics and their effect in respiratory allergy. According to published data, probiotics could emerge as a novel, complementary treatment option for allergic rhinitis but not for asthma.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".