Is Adenotonsillar Hypertrophy Associated With Dentofacial Morphology༟
Bibliographic record
Abstract
Aim or purpose To summarize the existing evidence regarding the association between adenotonsillar hypertrophy and dentofacial characteristics of children. Materials and methods Four databases (PubMed, Embase, Web of Science and VIP Chinese Journal Database) were searched from inception to November 1, 2024 for cross-sectional studies that compared the dental or craniofacial characteristics of children with and without adenoid and/or tonsil hypertrophy. The Newcastle-Ottawa Scale for Cross-Sectional Studies was used to assess the methodological quality of included studies. Meta-analyses were performed with the random-effects model. Results Thirty-six studies were included in this review. The mandibular plane angle (P<0.00001), articular angle (P<0.0001) were significantly greater in children with adenoid and/or tonsil hypertrophy. No significant differences were found between the ANB angle between the two groups (P=0.59). The SNA (P=0.01) and SNB angle (P=0.005) were found to be significantly smaller in children with adenoid and/or tonsil hypertrophy. Regarding dental characteristics, the rate of Angle Class II and Class III malocclusions (P<0.0001) and open bite (P=0.001) were found to be higher in the adenoid and/or tonsil hypertrophy children. In addition, the upper arch width (P=0.0008) were found to be smaller in isolated tonsillar hypertrophy children. Conclusions Based on evidence of low to very low certainty, children with ATH tend to exhibit craniofacial characteristics such as sagittal maxillary and mandibular retrognathia and an increased mandibular plane angle.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".