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Record W4309109529 · doi:10.1101/2022.11.11.516128

Molecular autism research in Africa: a scoping review comparing publication outputs to Brazil, India, the UK, and the USA

2022· review· en· W4309109529 on OpenAlexfundno aff
Emma Frickel, Sophia Bam, Erin Buchanan, Caitlyn Mahony, Mignon van der Watt, Colleen O’Ryan

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typereview
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
FundersWorld Health OrganizationOntario Water ConsortiumUNICEFWorld Bank Group
KeywordsEconomic shortageLow and middle income countriesAutismPer capitaMental healthMedicineGeographyPolitical sciencePsychiatryEconomic growthDeveloping countryEnvironmental healthPopulationGovernment (linguistics)

Abstract

fetched live from OpenAlex

ABSTRACT The increased awareness of autism spectrum disorders (ASD) is accompanied by burgeoning ASD research, and concerted research efforts are trying to elucidate the molecular ASD aetiology. However, much of this research is concentrated in the Global North, with recent reviews of research in Sub-Saharan Africa (SSA) highlighting the significant shortage of ASD publications from this region. The most limited focus area was molecular research with only two molecular studies ever published from SSA, both being from South Africa (SA). We examine the molecular ASD research publications from 2016 to 2021 from all African countries, with a special focus on SA. The SSA publications are compared to Brazil and India, two non-African, low-to-middle-income countries (LMICs), and to the UK and USA, two high-income countries (HICs). There were 228 publications across all regions of interest; only three publications were from SA. Brazil (n=29) and India (n=27) had almost 10 times more publications than SA. The HICs had more publications than the LMICs, with the UK (n=62) and the USA (n=74) having approximately 20 to 25 times more publications than SA, respectively. Given that SA has substantial research capacity as demonstrated by its recent research on SARS-CoV-2, we explore potential reasons for this deficit in molecular ASD publications from SA. We compare mental health research outputs, GDP per capita, research and development expenditure, and the number of psychiatrists and child psychiatrists per 100,000 people across all regions. The UK and the USA had significantly higher numbers for all these indicators, consistent with their higher publication output. Among the LMICs, SA can potentially produce more molecular ASD research, however, there are numerous barriers that need to be addressed to facilitate increased research capacity. These include cultural stigmas, challenges in accessing mental healthcare, shortages of specialists in the public sector, and the unreliability of ASD diagnostic tools across the 11 official SA languages. The unique genetic architecture of African populations presents an untapped reservoir for finding novel genetic loci associated with ASD. Therefore, addressing the disparity in molecular ASD research between the Global North and SSA is integral to global advancements in ASD 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 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)0.023
metaresearch head score (Gemma)0.132
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
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 score0.939
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.132
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0610.073
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.130
GPT teacher head0.372
Teacher spread0.241 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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