Adverse event monitoring, assessment, and reporting in nutraceutical and phytoceutical trials for pediatric neuropsychiatric conditions: A systematic review
Bibliographic record
Abstract
BACKGROUND: Natural treatments may be used as an alternative or adjunct treatment for childhood neuropsychiatric disorders. Knowledge of benefits and harms is needed to inform use guidelines. We aimed to systematically identify how and which adverse events are monitored, assessed, and reported in pediatric trials that tested nutraceutical and phytoceutical treatments. METHODS: We searched MEDLINE, Embase, PsycINFO, ProQuest Dissertations and Theses Global, Cochrane Library, and Google Scholar from 2012 to 2024. Eligible studies included nutraceutical and phytoceutical trials, experimental or quasi-experimental in design, involving children or adolescents (age 4-19 years) with neuropsychiatric conditions. RESULTS: Ninety-eight trials were included with 75 reported as completed (77%). The most common natural treatment tested was polyunsaturated fatty acids (36%, 35/98). Most trials focused on treating attention-deficit/hyperactivity disorder (59%, 58/98) or autism spectrum disorder (21%, 21/98). Investigators from 74/98 trials (76%) reported methods that indicated adverse event monitoring. For these trials, events defined a priori for monitoring were identified in 43% (32/74), methods for collecting and recording events were described in 68% (50/74), and assessment of event severity and attribution was described in 49% (36/74) and 26% (19/74), respectively. Over 100 different adverse events were reported across completed trials. The most common events reported were gastrointestinal distress (65%, 49/75) and headache (33%, 25/75). CONCLUSIONS: We found variability in monitoring, assessing, and reporting adverse events in pediatric trials of natural treatments. The adverse events identified in this review reinforces that specific events should be prospectively monitored in future trials.
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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 | 0.042 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".