Sleep Disturbances in Children and Adolescents With Non–Dialysis-Dependent Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: While studies have shown sleep disorders to be common in adults with chronic kidney disease (CKD), pediatric data are scarce. OBJECTIVE: To characterize the prevalence of sleep disorders among children and adolescents with non-dialysis-dependent CKD. DESIGN: Prospective, questionnaire-based, cross-sectional study. SETTING: Tertiary pediatric nephrology center. PARTICIPANTS: Children aged 6 to 18 years with non-dialysis-dependent CKD. Those with renal transplants were also considered to have CKD and were included, provided it was at least 3 months after the transplant. INTERVENTIONS: A validated pediatric sleep questionnaire. OUTCOME MEASURES: Four domains of sleep disturbance were assessed: sleep-disordered breathing, restless leg syndrome/paroxysmal leg movement (RLS/PLM), insomnia, and excessive daytime sleepiness. Positive responses to any of these signified the presence of a sleep disorder. RESULTS: A total of 49 non-dialysis-dependent children (30 with non-renal transplant CKD and 19 with post-renal transplant CKD; median age, 14 years; interquartile range, 6-18 years) were administered the pediatric sleep questionnaire; 71% (n = 35) of the patients were male; 37% (n = 18) were identified as having a sleep disorder; 40% (n = 12) were in the nontransplant CKD group and 32% (n = 6) in the transplant CKD group. The most common type of sleep disorder was RLS/PLM, affecting 27% (n = 8) in the nontransplant CKD group and 32% (n = 6) in the transplant CKD group. There was no correlation between stage of CKD and prevalence of sleep problems (P = .22). CONCLUSIONS: Disordered sleep was identified in more than one-third of our study population, and the most common type was RLS/PLM. Pediatricians should be aware of the relatively high incidence of sleep disorder among children and adolescents with CKD.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".