Prevalence of Autism Spectrum Disorder (ASD) in all Individuals Diagnosed with Down Syndrome (DS): A Systematic Review and Meta-analysis Protocol
Bibliographic record
Abstract
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.
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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 | 0.064 | 0.086 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.025 | 0.031 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".