Montelukast Is Not Effective in Controlling Allergic Symptoms Outside the Airways
Bibliographic record
Abstract
BACKGROUND: Subjects with atopic syndrome often perceive symptoms from various organs. A single drug that acts on all the syndrome's manifestations would be the ideal treatment. The role of montelukast, a cysteinyl-leukotriene receptor antagonist, is established in treating allergic rhinitis and asthma, but its ability to alleviate atopic symptoms outside the airways is controversial. Our aim was to assess if montelukast could be used to treat all the various symptoms seen in subjects with atopic syndrome. METHODS: A randomised, double-blind, placebo-controlled crossover study on the effect of montelukast in atopic syndrome was conducted during the 2007 pollen season. Forty-five pollen-sensitised subjects who had allergic symptoms from both the upper and lower airways and allergic symptoms outside the airways (conjunctivitis, oral symptoms, eczema and/or urticaria) were recruited. The primary outcome parameter was the allergic symptoms, which were assessed using a questionnaire. Secondary outcome parameters were lower-airway inflammation (exhaled nitric oxide) and the need for rescue medication (inhaled beta2-agonists and oral antihistamines). RESULTS: There were no differences between montelukast and placebo treatments in allergic symptoms, in exhaled NO concentration or in the need for oral antihistamines. The need for inhaled beta2-agonists was significantly lower during montelukast treatment. CONCLUSIONS: Montelukast was not effective in treating allergic symptoms outside the airways in subjects suffering from different manifestations of the atopic syndrome. Based on the current results, montelukast should not be recommended as a general drug to treat all the symptoms of atopic syndrome, but it should be considered as a drug for asthma and rhinitis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".