Treatment Approaches for Functional Neurological Disorders in Children
Bibliographic record
Abstract
Abstract Purpose of Review Functional neurological disorder (FND) is a multi-network brain disorder that encompasses a broad range of neurological symptoms. FND is common in pediatric practice. It places substantial strains on children, families, and health care systems. Treatment begins at assessment, which requires the following: themedicaltask of making the diagnosis, theinterpersonaltask of engaging the child and family so that they feel heard and respected, thecommunicationtask of communicating and explaining the diagnosis, and thelogisticaltask of organizing treatment. Recent Findings Over the past decade, three treatment approaches—Retraining and Control Therapy (ReACT), other cognitive-behavioral therapies, and multidisciplinary rehabilitation—have been evaluated in the USA, Canada, and Australia. Of children treated in such programs, 63 − 95% showed full resolution of FND symptoms. The common thread across the programs is their biopsychosocial approach—consideration of biological, psychological, relational, and school-related factors that contribute to the child’s clinical presentation. Summary Current research strongly supports a biopsychosocial approach to pediatric FND and provides a foundation for a stepped approach to treatment. Stepped care is initially tailored to the needs of the individual child (and family) based on the pattern and severity of FND presentation. The level of care and type of intervention may then be adjusted to consider the child’s response, over time, to treatment or treatment combinations. Future research is needed to confirm effective treatment targets, to inform the development of stepped care, and to improve methodologies that can assess the efficacy of stepped-care 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".