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Record W3161573753 · doi:10.1089/cap.2020.0110

Psychotropic Polypharmacy Among Children and Youth with Autism: A Systematic Review

2021· review· en· W3161573753 on OpenAlexaff
Chantel Ritter, Katherine Hewitt, Carly A. McMorris

Bibliographic record

VenueJournal of Child and Adolescent Psychopharmacology · 2021
Typereview
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryUniversity of Guelph
Fundersnot available
KeywordsPolypharmacyAutismPsychiatryMedicineAggressionPopulationPsychologyAdverse effectClinical psychologyEnvironmental healthIntensive care medicinePharmacology

Abstract

fetched live from OpenAlex

Objectives: Majority of youth with autism are taking two or more medications (psychotropic or nonpsychotropic) simultaneously, also known as polypharmacy. Yet the efficacy and the potential outcomes of polypharmacy in this population are widely unknown. This systematic literature review described the trends of polypharmacy among autistic youth, and identified factors associated with polypharmacy. Methods: Sixteen studies were included, encompassing over 300,000 youth with autism. Results: Rates of polypharmacy varied quite substantially across studies, ranging from 6.8% to 87% of autistic youth. Having psychiatric comorbidities, self-injurious behaviors, and physical aggression, as well as being male and older, were associated with higher rates of polypharmacy. Conclusion: Findings emphasize the importance of further research to determine appropriate practices related to the monitoring of adverse side effects, and the long-term impact of polypharmacy among autistic youth.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0070.007
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.028
GPT teacher head0.350
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations38
Published2021
Admission routes1
Has abstractyes

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