Pediatric obstructive sleep apnea—Dental professionals can play a crucial role
Bibliographic record
Abstract
The significant contribution of dental professionals to the management of selected adult obstructive sleep apnea (OSA) cases is understood. Among children, it has also been suggested that dental professionals may also help screen and manage this morbidity in selected cases. It has also been noted that our understanding of pediatric OSA lags significantly behind adult OSA. During the screening process for potential pediatric OSA cases, dental professionals may be quite helpful as specific craniofacial abnormalities have been previously associated with pediatric OSA, including Class II malocclusion, vertical facial growth and maxillary transversal deficiency. As dental professionals assess children more frequently than physicians, they can help screen sleep-disordered breathing signs and symptoms using validated questionnaires. In more advanced cases, orthodontists may be leading contributors to the management of selected cases where a craniofacial involvement is suspected. Rapid maxillary expansion and mandibular or maxillary anterior repositioning devices have been proposed as managing alternatives. So far, there is no substantial evidence if these approaches can be adopted to treat OSA fully or if the reported OSA signs and symptoms improvements observed in a selected group of patients are stable long-term. Nevertheless, dentists and orthodontists' integration into a transdisciplinary team should be encouraged to play a significant role. This review discusses dentists or orthodontists' potential contribution to screen and manage selective pediatric OSA patients as part of a transdisciplinary team.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".