Adverse effects of psychotropic medications in children: predictive factors.
Bibliographic record
Abstract
OBJECTIVE: Despite limited information related to efficacy in children, psychotropic medications are commonly prescribed as a first-line treatment for a range of psychiatric diagnoses in children in a variety of clinical settings. Usage has increased over the past three decades. Although psychotropic medications are often effective at treating psychiatric symptoms, the risk of adverse effects (AE) in children is unclear. The current research seeks to identify the mental health characteristics of those children at highest risk of experiencing potential AE from psychotropic medications. METHODS: Psychotropic medication monitoring checklists were used to record possible AE for 99 pediatric clients in a tertiary mental health residential treatment centre for the duration of one to eight weeks. Client characteristics, including the number of diagnoses and behavioural variables, were explored for predictive value of potential AE observed. RESULTS: Results showed that the total number of potential AE was positively predicted by the number of DSM-IV categories diagnosed, as well as behavioural symptoms of impulsiveness and uncooperativeness. CONCLUSIONS: The findings of this study indicate that the number of potential AE from psychotropic medications may be predictable based on client characteristics. Predicting this likelihood during initial assessment can be useful in directing and monitoring treatment, as well as preventing serious events related to medication use.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".