Efficacy and safety of subcutaneous and sublingual allergen immunotherapy in the treatment of asthma in children: a systematic review and meta-analysis
Bibliographic record
Abstract
Asthma is a common chronic condition in children globally. Allergen-specific immunotherapy, such as subcutaneous (SCIT) and sublingual (SLIT) therapies, are promising by increasing allergen tolerance. This meta-analysis compares the efficacy and safety of SLIT and SCIT in pediatric asthma. We searched PubMed, Cochrane Library, and Embase for randomized controlled trials and case–control studies comparing SLIT and SCIT in asthmatic children. Meta-analysis was conducted using random-effects models with calculations via R software version 4.3.2 and RevMan version 5.4. Study quality and bias risk were assessed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool. The literature search yielded a total of 1787 records, with 7 studies meeting the inclusion criteria after screening and assessments. There was no significant difference in the Total Asthma Symptoms Score between SLIT and SCIT (mean difference −0.05 [95% CI: −0.21; 0.10]). However, asthma improvement rates were higher in the SLIT group (risk ratio 0.77 [95% CI: 0.64; 0.93]). FEV1 improvement showed no significant difference (mean difference −1.60 [95% CI: −6.27; 3.08]). Adverse events were similar between the treatments (risk ratio 0.56 [95% CI: 0.11; 2.82]). SLIT and SCIT were generally similarly effective and safe for treating pediatric asthma. SLIT may be preferred due to its noninvasive administration. More research is needed on long-term effects and tailored treatment approaches.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".