Efficacy of Melatonin in Children With Postconcussive Symptoms: A Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Approximately 25% of children with concussion have persistent postconcussive symptoms (PPCS) with resultant significant impacts on quality of life. Melatonin has significant neuroprotective properties, and promising preclinical data suggest its potential to improve outcomes after traumatic brain injury. We hypothesized that treatment with melatonin would result in a greater decrease in PPCS symptoms when compared with a placebo. METHODS: We conducted a randomized, double-blind trial of 3 or 10 mg of melatonin compared with a placebo (NCT01874847). We included youth (ages 8-18 years) with PPCS at 4 to 6 weeks after mild traumatic brain injury. Those with significant medical or psychiatric histories or a previous concussion within the last 3 months were excluded. The primary outcome was change in the total youth self-reported Post-Concussion Symptom Inventory score measured after 28 days of treatment. Secondary outcomes included change in health-related quality of life, cognition, and sleep. RESULTS: Ninety-nine children (mean age: 13.8 years; SD = 2.6 years; 58% girls) were randomly assigned. Symptoms improved over time with a median Post-Concussion Symptom Inventory change score of -21 (95% confidence interval [CI]: -16 to -27). There was no significant effect of melatonin when compared with a placebo in the intention-to-treat analysis (3 mg melatonin, -2 [95% CI: -13 to 6]; 10 mg melatonin, 4 [95% CI: -7 to 14]). No significant group differences in secondary outcomes were observed. Side effects were mild and similar to the placebo. CONCLUSIONS: Children with PPCS had significant impairment in their quality of life. Seventy-eight percent demonstrated significant recovery between 1 and 3 months postinjury. This clinical trial does not support the use of melatonin for the treatment of pediatric PPCS.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".