Systematic review of assessment instruments measuring outcomes in psychological interventions for pediatric functional neurological disorders
Bibliographic record
Abstract
OBJECTIVE: This systematic review focused on identifying and evaluating assessment tools used to measure outcomes of psychological interventions for pediatric functional neurological disorder (FND). METHOD: A comprehensive search was conducted on September 24, 2024, across Web of Science, PsycINFO, and Medline. Studies were included if they involved individuals under 18 with FND diagnosis, utilized a psychological intervention, and assessed treatment outcomes using validated measures. Sixteen studies qualified for inclusion, and 26 different assessment instruments were identified. These were assessed against the Core Outcome Measures in Effectiveness Trials framework, covering symptoms, life impact, and resource utilization. The psychometric characteristics of these assessment tools were examined through further searches which concentrated on reliability, validity, and factorial invariance. The Joanna Briggs Institute critical appraisal tools were used to evaluate the risk of bias. Findings were synthesized narratively due to the descriptive and exploratory nature of the research aims. RESULTS: No outcome assessment tools designed specifically for pediatric FND populations were identified. Most studies employed measures targeting mental health symptoms and life impact, however, none of these tools were validated with FND samples, and several lacked validation in pediatric populations. CONCLUSIONS: This review highlights significant gaps, including the need for psychometric assessment of tools and validation studies on FND samples. The current evidence does not support recommending FND-specific measures due to their limited development and validation. Instead, using existing questionnaires validated on broader pediatric populations is recommended. Research should prioritize the validation of measures in FND populations to establish more robust, standardized tools for clinical and research 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 | 0.022 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".