Clozapine Impact on Clinical Outcomes and Aggression in Severely Ill Adolescents with Childhood-Onset Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of clozapine on aggressive behaviour and clinical outcomes in children and adolescents with schizophrenia or schizoaffective disorder. METHODS: We reviewed the charts of 6 children and adolescents who were admitted consecutively to a long-term care facility for clinical outcomes, including seclusion and restraints incidents prior to and during clozapine treatment. We also present a representative case history. RESULTS: We noted clinically significant improvements in social interaction and decreases in the number of violent episodes and homicidal or suicidal thoughts. The global assessment of functioning (GAF) scores improved significantly. Weight gain was significant. CONCLUSIONS: These cases illustrate the benefits of clozapine treatment in refractory childhood-onset schizophrenia. Outcomes are similar to those described in adults. Even though open data limit conclusions from this study, it is pertinent that there was a clinically significant improvement in aggressive behaviours. This may be particularly important for improved morale of patients, their families, and treating staff. It may also be helpful in discharge to a less restrictive environment.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".