Children passive smoking jeopardises the efficacy of standard anti-allergic pharmacological therapy, while sublingual immunotherapy withstands
Bibliographic record
Abstract
BACKGROUND: The association between genetic predisposition and environmental risk factors such as passive smoke in determining respiratory allergies is still uncertain; even less is known about the role played by passive smoking in influencing the success of therapy for rhinitis and allergic asthma. OBJECTIVE: The purpose of this prospective, randomised study was to determine whether passive smoking influences the outcome of therapies in paediatric patients with allergic respiratory diseases. METHODS: The study included 68 children (mean age 11.51 years; range: 5-17) suffering from perennial rhinitis and intermittent asthma monosensitised to Dermatophagoides. Thirty-four subjects were exposed to daily passive smoking in their families, 34 were not. The two groups have been then randomised to receive continuous treatment with cetirizine or SLIT for three years. RESULTS: There were 3/34 (8.8%) dropouts in the SLIT arm and 4/34 (11.7%) in the cetirizine arm. After three years, the patients exposed to passive smoking showed higher nasal eosinophilia, a worse clinical-symptomatic and pharmacological score with a worsened bronchial reactivity and functional indices of persistent asthma, regardless of how they had been treated. Nevertheless, SLIT prevented the worsening of all the clinical parameters more than the antihistamine alone either among the children exposed to smoking or not. CONCLUSIONS: Exposure to passive smoking in children suffering from respiratory allergies due to Dermatophagoides decreased the clinical response to both drug therapy and SLIT. Nonetheless, while the children submitted to drug therapy worsened or did not show any significant improvement, the ones treated with SLIT improved.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".