Stopping inappropriate medication of children with intellectual disability, autism or both: the STOMP–STAMP initiative
Bibliographic record
Abstract
SUMMARY Children with intellectual disability are often prescribed psychotropic medication to manage behaviours that challenge. Unfortunately, many receive medication with potentially serious long-term side-effects that has been prescribed inappropriately or for longer than is necessary. NHS England launched STOMP (stopping the over-medication of people with intellectual disability, autism or both with psychotropic medicines) in 2016 to reduce the inappropriate prescribing in adults. This was broadened to include children in 2018 by the addition of STAMP (supporting treatment and appropriate medication in paediatrics). In this article we review the rationale for STOMP–STAMP, highlight the Royal College of Psychiatrists’ position statement on STOMP–STAMP and give clinical advice for psychiatrists who treat children with intellectual disability, autism and/or attention-deficit hyperactivity disorder (ADHD). Importantly, it is essential to consider that ADHD may have been missed and that by diagnosing and treating it, the need for inappropriate antipsychotic medication may be reduced.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".