Impact of adenotonsillectomy and maxillary expansion on the apnea-hypopnea index and minimum oxygen saturation in non-obese pediatric obstructive sleep apnea with relatively normal sagittal and vertical craniofacial features: a cross-over randomized controlled trial
Bibliographic record
Abstract
Objective: To determine the impact and best management sequence between adenotonsillectomy (AT) and rapid maxillary expansion (RME) on the apnea-hypopnea index (AHI) and minimum oxygen saturation (MinSaO 2 ) in non-obese pediatric obstructive sleep apnea (OSA) patients presenting relatively normal sagittal and vertical craniofacial features. Study Design/Methods: Thirty-two children with a mean age of 8.8 years, with a graded III/IV tonsillar hypertrophy and maxillary constriction, participated in a cross-over randomized controlled trial. As the first intervention, one group underwent AT while the other underwent RME. After six months, interventions were switched in those groups, but only to participants with an AHI > 1 after the first intervention. Polysomnography (PSG) was conducted before (T 0 ), six months after the first (T 1 ) and the second (T 2 ) intervention. The influence of sex, adenotonsillar hypertrophy degree, initial AHI and MinSaO 2 severity, and intervention sequence were evaluated using linear regression analysis. Intra- and inter-group comparisons for AHI and MinSaO 2 were performed using ANOVA and Tukey´s test. Results: The initial AHI severity and intervention sequence (AT first) explained 94.9% of AHI improvement. AT caused more significant AHI improvements than RME. The initial MinSaO 2 severity accounted for 83.1% of MinSaO 2 improvement changes. Most AHI reductions and MinSaO 2 improvements were due to AT than RME. In most cases, RME had a marginal effect on AHI and MinSaO 2 when adjusted for confounders. Conclusions: Initial AHI severity and AT as the first intervention accounted for most of the AHI improvement. The initial MinSaO 2 severity alone accounted for the most changes in MinSaO 2 increase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".