Prevalence of laryngomalacia in children presenting with sleep‐disordered breathing
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of laryngomalacia among children presenting with symptoms of sleep-disordered breathing (SDB). METHOD: A retrospective observational study was conducted at a tertiary care paediatric hospital. All children presenting with SDB during a 55-month period were investigated using sleep nasopharyngoscopy (SNP). Patients who had laryngomalacia were identified. Patients who did not present primarily with SDB, or were not examined with SNP were excluded. Data for analysis was collected from a prospectively kept surgical database and medical records. This included patients' demographics, symptoms (including symptoms in infancy), diagnoses, SNP findings, overnight pulse oximetry findings, and treatment. RESULTS: We identified 358 patients with documented primary diagnosis of SDB and who had undergone SNP. Fourteen of these also had a documented diagnosis of laryngomalacia, giving a prevalence rate of 3.9%. Three children were syndromic, and one had cerebral palsy in addition to SDB and laryngomalacia. Three children were obese, and three children had gastroesophageal reflux disease. Seven cases (50%) had symptoms of snoring and/or swallowing dysfunction and/or stridor in infancy. Twelve patients had adenotonsillar surgery. In eight cases symptoms resolved completely with adenotonsillar surgery only. In total, six patients had a supraglottoplasty. There were three failures to supraglottoplasty. CONCLUSION: The prevalence of laryngomalacia within children presenting with SDB is 3.9%. Our findings support full evaluation of the airway to identify the site of pathology mediating SDB symptoms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".