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
OBJECTIVE: 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. METHODS: 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. RESULTS: 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]). CONCLUSIONS: 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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| 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".