ASSOCIATION BETWEEN ADENOID HYPERTROPHY AND CHRONIC RHINOSINUSITIS IN CHILDREN: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Introduction. Adenoid hypertrophy (AH) and chronic rhinosinusitis (CRS) are common pediatric conditions, with a prevalence of approximately 34% and 2–4%, respectively. The anatomical proximity of the pharyngeal tonsil to the nasal cavity and the similarity in clinical presentations raise questions regarding a potential association between these pathologies. Establishing correlations between adenoid hypertrophy and chronic rhinosinusitis may assist clinicians in early diagnosis and the implementation of individualized treatment approaches. Aim. To conduct a comprehensive evaluation and critical systematic analysis of existing scientific literature to determine the association between adenoid hypertrophy and chronic rhinosinusitis in children. Materials and methods. A systematic search was conducted in the PubMed/MEDLINE, Embase, Cochrane Library, Web of Science, Scopus, and Google Scholar databases covering the period from 1975 to December 2023. The search included the Keywords: “adenoid hypertrophy,” “adenoid vegetation,” “chronic rhinosinusitis,” “chronic sinusitis.” Study quality was assessed using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias Tool. The review followed PRISMA guidelines. Results. A total of 41 studies were analyzed, with sample sizes ranging from 16 to 12,965 children (mean age 3.3–12.62 years). Shared etiological pathogens were identified, including Staphylococcus aureus, Haemophilus influenzae, Streptococcus pneumoniae, and Moraxella catarrhalis. Biofilms were detected in 94.9% of CRS cases with AH compared to 1.9% in cases of obstructive sleep apnea syndrome (OSAS). Elevated levels of pro-inflammatory mediators (TGF-β1, sIL-2R, MMP-2, MMP-9, TIMP-1) and decreased levels of protective factors (IgA, BLIMP-1, SP-D) were reported. Impaired mucociliary clearance was demonstrated by reduced ciliary beat frequency (5.35 ± 1.06 Hz vs. 6.37 ± 0.71 Hz in controls). Adenoidectomy showed efficacy in 47–88.3% of cases in reducing CRS symptoms. Conclusions. Most studies confirm a strong association between adenoid vegetations and chronic rhinosinusitis in children, driven by common etiopathogenetic mechanisms. Adenoidectomy may be considered a first-line treatment for uncomplicated pediatric CRS. It is proposed to classify adenoid vegetations as a distinct phenotypic subtype within pediatric CRS classification.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".