Topical Nasal Steroids for Allergic Rhinitis in Children. Is One Better Than the Others? Protocol for a Systematic Review and Network Meta-analysis.
Bibliographic record
Abstract
Background The present protocol is registered in the International Prospective Register of Systematic Reviews PROSPERO under de ID code CRD42023476313, and adheres the PRISMA-P 2015 statement. The complete checklist can be consulted in the extended data section. Allergic Rhinitis (AR) is the most common chronic disease in children worldwide. According to many guidelines topical nasal steroids (TNS) are the first-line treatment for moderate/severe AR in children, nevertheless there is no conclusive evidence about which of them is superior in terms of efficacy and safety, including their impact on child’s growth. Methods We will conduct a systematic review of randomized controlled trials evaluating TNS in the treatment of children with moderate/severe AR. The primary outcome is the efficacy measured with the Total Nasal Symptom Severity Score (TNSS). Secondary outcomes are quality of life, adverse events (AE), and growth velocity. We will search Medline, Embase, CENTRAL, LILACS, clinicaltrials.gov, WHO trials database and grey literature resources. Two reviewers will independently screen titles and abstracts, review full texts, extract information, and assess the risk of bias and the confidence in the estimate (with the grading of recommendations, assessment, development, and evaluation [GRADE] approach). We will conduct a random-effects NMA to combine the pooled direct and indirect treatment effect estimates for each outcome if adequate data is available and transitivity and coherence assumptions are considered justifiable. Subgroup and sensitivity analyses are planned to evaluate the impact of some potential effect modifiers such as doses, co-intervention with oral antihistamines, the type of AR (intermittent or persistent), sponsorship of the study, the time of outcomes measurement and the risk of bias. We will use GRADE approach to draw conclusions from NMA. Discussion This systematic review and network meta-analysis aims to determine the relative efficacy and safety of the TNS for treatment of children with moderate/severe AR. The results will provide valuable information to assist clinicians, guideline developers, patients, and policy makers about the management of these patients, based on the best available evidence. Systematic review registration: PROSPERO number: CRD42023476313.
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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 | 0.065 | 0.104 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.028 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.104 | 0.009 |
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".