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Prevalence of Autism Spectrum Disorder (ASD) in all Individuals Diagnosed with Down Syndrome (DS): A Systematic Review and Meta-analysis Protocol

2022· review· en· W4296826638 on OpenAlexaff
Rudaina Banihani

Bibliographic record

VenueJournal of Clinical and Medical Research · 2022
Typereview
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsAutism spectrum disorderAutismCINAHLPopulationMEDLINEPsychiatryClinical psychologyDown syndromeMedicineCritical appraisalIncidence (geometry)Meta-analysisPsychologyPediatricsPsychological interventionAlternative medicinePathology

Abstract

fetched live from OpenAlex

Background: Traditionally, autism spectrum disorder in people with Down syndrome was believed to be uncommon. This misconception is rooted in the challenges that a dual diagnosis poses. In fact, evidence indicates that children with Down syndrome are at risk for autism spectrum disorder with a potentially higher prevalence than the typically developing population. The purpose of this review is to determine the reported prevalence rate of autism spectrum disorder in all individuals with Down syndrome in comparison to the prevalence rate of autism spectrum disorder in the typical population when specific diagnostic tools are used. Methods: A systematic review will be conducted of the prevalence and incidence data and perform a meta-analysis of these results. This study will consider all studies that reported on children and adults with an existing diagnosis of Down syndrome and diagnosed by the standardized assessments for autism spectrum disorder. The diagnoses made by team assessment (psychologist, psychiatrist & developmental pediatrician) will also be considered according to DSM-III, DSM-IV or DSM-V criteria for diagnosing autism spectrum disorder or if they use autism spectrum disorder screeners. Studies will be considered from all countries that have data reporting prevalence on this topic. The language restrictions will not be applied attempting to translate studies that are not in English. The five databases (MEDLINE, Embase, PsychINFO, Scopus, and CINAHL) will be searched. Two reviewers will conduct all screening and data extraction independently. The articles will be categorized according to key findings and a critical appraisal performed. Discussion: The results of this review will bring increased awareness of the presence of autism spectrum disorder in individuals with Down syndrome. In doing so, this may facilitate a recommendation for screening and diagnosis of autism spectrum disorder in all individuals with Down syndrome. Based on the research demonstrating the benefits of early identification and intervention on the outcomes of children with autism, we anticipate similar benefits in this population. This will guide the allocation of resources and direct future research.

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 imitation

Not 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.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.049
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.720
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0490.049
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0130.002
Bibliometrics0.0020.004
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.007
Insufficient payload (model declined to judge)0.0060.000

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 head0.372
GPT teacher head0.556
Teacher spread0.184 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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