Review: Adverse event monitoring and reporting in studies of pediatric psychosocial interventions: a systematic review
Bibliographic record
Abstract
BACKGROUND: Adverse event monitoring in studies of psychotherapy is crucial to clinical decision-making, particularly for weighing of benefits and harms of treatment approaches. In this systematic review, we identified how adverse events are defined, measured, and reported in studies of psychosocial interventions for children with mental disorders. METHOD: Medline, PsycINFO, Embase, ProQuest Dissertations and Theses Global, and the Cochrane Library were searched from January 2011-January 2023, and Google Scholar from January 2011-February 2023. English language experimental and quasi-experimental studies that evaluated the efficacy or effectiveness of psychosocial interventions for childhood mental disorders were included. Information on the definition, assessment, and report of adverse events was extracted using a checklist based on Good Clinical Practice guidelines. RESULTS: In this review, 117 studies were included. Studies most commonly involved treating anxiety disorders or obsessive-compulsive disorder (32/117; 27%); 44% of the experimental interventions tested (52/117) were cognitive behavioral therapies. Adverse events were monitored in 36 studies (36/117; 31%) with a protocol used in 19 of these studies to guide monitoring (19/36; 53%). Twenty-seven different events were monitored across the studies with hospitalization the most frequently monitored (3/36; 8%). Event severity was fully assessed in 6 studies (17%) and partially assessed in 12 studies (33%). Only 4/36 studies (11%) included assessing events for cause. CONCLUSIONS: To date, adverse events have been inconsistently defined, measured and reported in psychosocial intervention studies of childhood mental health disorders. Information on adverse events is an essential knowledge component for understanding the potential impacts and risks of therapeutic interventions.
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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 | 0.036 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".