Treatment and persistence/recurrence of sleep‐disordered breathing in children with Down syndrome
Bibliographic record
Abstract
OBJECTIVE: Sleep-disordered breathing (SDB) is common in children with Down syndrome, but the trajectory and long-term outcomes are not well-described. In a retrospective longitudinal cohort of children with Down syndrome, study objectives were to (1) characterize polysomnography (PSG), treatments received, and persistence/recurrence of SDB and (2) explore predictors of SDB persistence/recurrence. METHODS: A retrospective cohort study was conducted of children who underwent PSGs between 2004 and 2014. SDB was defined as obstructive sleep apnea (OSA)-mixed (apnea-hypopnea index [AHI] >5 events/hour), central sleep apnea or hypoventilation. PSGs, interventions, and trajectory of SDB were described. Age, body mass index (BMI) Z-score and AHI at first SDB diagnosis were evaluated as predictors of persistent/recurrent SDB. RESULTS: Of 506 children, 120 had ≥1 PSG; 54 had subsequent PSGs. Children with ≥2 PSGs were more likely to have higher total AHI (P = .02) and obstructive-mixed AHI (P = .01). Thirty-five of fifty-four (65%) were initially diagnosed with OSA-mixed SDB. After first PSG, 67 of 120 had OSA-mixed SDB, of whom 25 (37.3%) underwent adenotonsillectomy (T&A), 13 (19.4%) received positive airway pressure (PAP). Those who underwent T&A after PSG were significantly younger than those who received PAP (median age 6.2 vs 12.5 years; P = .005). OSA-mixed SDB persisted/recurred in 33 of 54 (73.3%) with ≥2 PSGs. Persistence/recurrence was not associated with age, AHI or BMI Z-score at first SDB. CONCLUSION: Children with Down syndrome undergoing T&A for SDB were significantly younger than those treated with PAP. SDB persisted/recurred in three of four and was not predicted by age, SDB severity or BMI Z-score. Longitudinal PSG assessment for persistence/recurrence of SDB is required in this population.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".