Central Sleep Apnea in Children—10 Years Experience at a Tertiary Sleep Laboratory
Bibliographic record
Abstract
Objective: Central sleep apnea (CSA) is a rare condition in children; however, it can cause significant morbidity if not diagnosed early. We aimed to increase the knowledge about CSA in children by describing the clinical characteristics of children diagnosed with CSA at our sleep center. Material and Methods: We retrospectively reviewed 1263 polysomnographies (PSG) performed between 2012 and 2023 at our tertiary sleep center and evaluated the clinical characteristics of the patients with CSA. Underlying diseases, clinical symptoms, sleep parameters, and short-term management of the patients were recorded. Results: Of the 1263 patients aged between 1 month and 18 years, 122 (9.65%) had CSA, with 54.9 % (n = 67) of them being female. Only 56.6% (n = 69) of the patients' parents had reported a symptom indicating sleep-disordered breathing. The most common underlying disease was genetic, including Down and Prader-Willi syndromes, followed by neurological diseases . Obstructive sleep apnea was detected in addition to CSA in 103 of the patients (84.4%). Bi-level positive airway pressure with a backup rate was the most common treatment modality. Conclusion: While CSA is a rare clinical condition in children, it occurs more commonly in those with an underlying disease. Awareness of the disease and timely referral of the patients for sleep studies are critical to prevent long-term sequelae.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".