Deprescribing in a Youth with an Intellectual Disability, Autism, Behavioural Problems, and Medication-Related Obesity: A Case Study.
Bibliographic record
Abstract
Vlad, not his real name, a 15 year old boy with an autism spectrum disorder and intellectual disability, was referred for psychiatric consultation due to aggression and other behavioural problems. He presented for initial psychiatric consultation on five psychotropic medications with associated severe obesity. A systematic deprescribing and cross-tapering plan was implemented, removing all five psychotropic medications (which included olanzapine and quetiapine) and introducing ziprasidone. These changes were associated with a 44.8kg weight loss with no behavioral deterioration and overall lower rates of aggression. Vlad's case may typify important deficiencies in the service system which create a context that allows for aggressive psychotropic polypharmacy without apparent concomitant increase in sophistication of behavioral management design and support, while also tolerating substantial treatment adverse effects (e.g., medication induced severe obesity) within a member of a vulnerable population (e.g., a youth with developmental disability in care). Suggestions to address some of these contextual factors are outlined.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".