Risperidone improves restricted, repetitive, and stereotyped behaviour in autistic children and adolescents
Bibliographic record
Abstract
McDougle CJ, Hollway J, Scahill L, et al . Risperidone for the core symptom domains of autism: results from the study by the autism network of the research units on pediatric psychopharmacology. Am J Psychiatry 2005;162:1142–8.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q Does risperidone improve repetitive behaviour and social and communication impairment in children and adolescents with autism? ### ![Graphic][5]</img>Design: Randomised controlled trial. ### ![Graphic][6]</img>Allocation: Not stated. ### ![Graphic][7]</img>Blinding: Double blind. ### ![Graphic][8]</img>Follow up period: Eight weeks. ### ![Graphic][9]</img>Setting: Five universities, USA; time period not stated. ### ![Graphic][10]</img>Patients: 101 children and adolescents aged 12–17 years with autism and impairing behavioural symptoms (DSM-IV, Autism Diagnostic Interview (revised); mean age 8.8 years; 82% male). Exclusions: not stated. ### ![Graphic][11]</img>Intervention: Risperidone (0.5 to 3.5 mg/day); placebo. ### ![Graphic][12]</img>Outcomes: Symptomatic behaviours (modified parent rated Ritvo-Freeman … [1]: {openurl}?query=rft.jtitle%253DAmerican%2BJournal%2Bof%2BPsychiatry%26rft.stitle%253DAm.%2BJ.%2BPsychiatry%26rft.aulast%253DMcDougle%26rft.auinit1%253DC.%2BJ.%26rft.volume%253D162%26rft.issue%253D6%26rft.spage%253D1142%26rft.epage%253D1148%26rft.atitle%253DRisperidone%2Bfor%2Bthe%2BCore%2BSymptom%2BDomains%2Bof%2BAutism%253A%2BResults%2BFrom%2Bthe%2BStudy%2Bby%2Bthe%2BAutism%2BNetwork%2Bof%2Bthe%2BResearch%2BUnits%2Bon%2BPediatric%2BPsychopharmacology%26rft_id%253Dinfo%253Adoi%252F10.1176%252Fappi.ajp.162.6.1142%26rft_id%253Dinfo%253Apmid%252F15930063%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1176/appi.ajp.162.6.1142&link_type=DOI [3]: /lookup/external-ref?access_num=15930063&link_type=MED&atom=%2Febmental%2F9%2F1%2F6.atom [4]: /lookup/external-ref?access_num=000229504300016&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif [9]: /embed/inline-graphic-5.gif [10]: /embed/inline-graphic-6.gif [11]: /embed/inline-graphic-7.gif [12]: /embed/inline-graphic-8.gif
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".