Identifying and treating catatonia in children with neurodevelopmental disorders: A case series.
Bibliographic record
Abstract
Catatonia is a neuropsychiatric syndrome that is an increasingly recognized cause of acute behavioural changes in children and adolescents with neurodevelopmental disorders (NDD). Literature suggests that catatonia can present differently in this population and can be missed due to diagnostic overshadowing. Catatonia is a treatable condition, and management strategies in children with NDD include benzodiazepines and electroconvulsive therapy (ECT). Untreated, it can cause significant morbidity including severe medical complications, and therefore timely recognition and management of catatonia in children and adolescents with NDD is essential. In this case series, we present three cases of children ages 7, 14, and 10, with diagnoses of autism spectrum disorder, Down syndrome, and Prader-Willi syndrome, respectively. All were admitted to a pediatric inpatient unit for acute behavioural regression. Each had symptoms consistent with catatonia, resulting in trials of benzodiazepine therapy with inadequate response, and were then treated with bilateral ECT. In all cases, marked improvement was noted after ECT, with no apparent adverse effects. The cases are used to highlight the nuances of diagnosis and management of catatonia in children and adolescents with NDD. This includes insights on how presentations of catatonia may differ in this population, challenges with the use of available diagnostic tools, and how these patients may respond differently to recommended treatments such as benzodiazepines. The case series aims to increase clinicians' awareness of pediatric catatonia when children and adolescents with NDD present with acute behavioural changes, and to encourage consideration of the full spectrum of treatments, including bilateral ECT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".