Melatonin Therapy of Pediatric Sleep Disorders: Recent Advances, Why it Works, Who are the Candidates and How to Treat
Bibliographic record
Abstract
Chronic sleep difficulties commonly coexist with neurodevelopmental and psychiatric problems. Children with special needs may have a variety of sleep disturbances and of these circadian rhythm sleep disorders appear to be the most common. Melatonin supplementation for some circadian rhythm sleep disorders is often an effective treatment because it corrects the associated abnormal melatonin secretion. Melatonin has a benign safety profile and significant potential health benefits. Melatonin has many functions including sleep promoting and chronobiotic properties. Melatonin therapy is only beneficial when persistent sleep difficulties are associated with low melatonin secretion and additional supplementation beyond the therapeutic dose does not result in further sleep promotion. Abnormal neurological modulation of pineal melatonin secretion is commonly present in neurodevelopmental and psychiatric disorders. Melatonin deficiency not only leads to sleep disturbance, but also to multiple health problems. Chronic sleep difficulties of special needs children must not be ignored because they may exacerbate the deficits in development which are already present. In this review, misconceptions about sleep and melatonin therapy, functions of this indoleamine, causes and diagnoses of circadian rhythm sleep disorders, physiological principles underlying treatment, selection of candidates, dose, safety and health benefits are discussed. Keywords: Children, melatonin, sleep disorder, therapy, disabilities
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".