L’influence du syndrome d’apnées obstructives du sommeil sur la décision thérapeutique orthodontique chez l’enfant et l’adolescent. Partie 2 : Quels traitements orthodontiques chez l’enfant apnéique ?
Bibliographic record
Abstract
Introduction: Sleep-disordered breathing could affect 10% of an orthodontic population. The integration of obstructive sleep apnea syndrome (OSAS) diagnosis could influence the choice of orthodontic techniques or their implementation, with the aim of improving ventilatory function. Material and Method: The author summarizes the clinical studies using dentofacial orthopedics, alone or in combination with other interventions, in pediatric OSAS or the repercussions of orthodontic interventions on upper airways. Results: For the same orthodontic anomaly, in particular, transverse maxillary deficiency, the temporality and the modality of treatment could be modified by a diagnosis of OSAS. It could be recommended to propose early orthopedic maxillary expansion, seeking to potentiate its skeletal effect, to reduce the severity of OSAS. Class II orthopedic devices have shown interesting results but the evidence value of the studies is not yet sufficient to recommend them widely and as an early treatment. Extractions of permanent teeth do not significantly reduce the upper airway. Discussion: OSAS in children and adolescents includes several endotypes and phenotypes for which orthodontics may or may not be indicated. It is not recommended to orthodontically treat an apneic patient with no significant malocclusion, for the sole purpose of having an effect on the respiratory tract. Conclusion: The orthodontic therapeutic decision is likely to be modified by a diagnosis of sleep-disordered breathing underlining the interest in systematic screening.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".