The latest on positive airway pressure for pediatric obstructive sleep apnea
Bibliographic record
Abstract
INTRODUCTION: Obstructive sleep apnea (OSA) is an important and evolving area in the pediatric population, with significant sequelae when not adequately managed. The use of positive airway pressure (PAP) therapy is expanding rapidly and is being prescribed to patients with persistent OSA post adenotonsillectomy as well as those children who are not surgical candidates including those with medical complexity. AREAS DISCUSSED: This article provides a state-of-the-art review on the diagnosis of pediatric OSA and treatment with positive airway pressure (PAP). The initiation of PAP therapy, pediatric interface considerations, PAP mode selection, administration and potential complications of PAP therapy, factors influencing PAP adherence, the use of remote ventilation machine downloads, considerations surrounding follow-up of patients post PAP initiation and evaluation of weaning off PAP will be reviewed. The literature search was conducted via PubMed, Cochrane Library and Google Scholar databases through to March 2024. EXPERT OPINION: Further research is required to address barriers to adherence. Further innovation of home monitoring devices for both the diagnosis and assessment of OSA is required, given the limited pediatric sleep medicine resources in several countries worldwide.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.011 | 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".