Clinical characteristics of children with autism spectrum disorders: a cross-sectional study of cases attended at three centers specializing in neurodevelopmental disorders in Kinshasa, Democratic Republic of Congo (DRC)
Bibliographic record
Abstract
Context and objective. Autism spectrum disorders (ASD) in sub-Saharan African (SSA) countries are poorly studied. The aim of the present study was to describe the socio-demographic and clinical characteristics of children with autism and to identify associated factors. Methods. This was a cross-sectional study of children with ASD attended at three specialized centers in Kinshasa. We confirmed a ASD diagnosis through clinical observation using Diagnostic and Statistical Manuel of Mental Disorders four Text Revision (DSM-VI-TR) criteria and standardized autism diagnostic tools. We analyzed socio-demographic and clinical characteristics and main comorbidities of ASD. The comparison of proportions was done using Pearson’s chi-square test. One-way ANOVAs were calculated to test differences in averages. Results. A total of 120 children (72.5 % male) were examined. Their mean age at diagnosis was 7.83 ± 3.4 years, while parents were alerted at 1.8 ±0.78 years. Language delays were the main alert sign (54%) and the main symptom (62%). Social interaction disorders (11.7 %) were under-reported by parents. The core signs of ASD were disorders of social interaction (90.5%), behavior (80%) and language (62.5%). The main ASD symptoms were associated with epilepsy (p=0.027), cerebral palsy (p=0.026) and hearing impairment (p=0.045). Conclusion. The diagnostic and language delay co-occurring with epilepsy and hearing impairment are the main clinical features of autism in the DRC. This study suggests that screening children for autism and its main comorbidities using a multidisciplinary approach should be a priority in Kinshasa. Contexte et objectif. Les troubles de spectre de l’autisme (TSA) en Afrique subsaharienne (ASS) sont très peu étudiés. L’objectif de la présente étude était d’examiner les caractéristiques socio-démographiques et clinique des enfants avec autisme et d’identifier les facteurs associés. Méthodes. Il s’agissait d’une étude transversale des enfants avec TSA des trois centres spécialisés à Kinshasa. Le diagnostic de l’autisme était confirmé par l’observation cliniques selon les critères diagnostic and Statistical Manuel of Mental Disorders four Text Revision (DSM-VITR) et les outils diagnostiques standardisés de childhood autism rating scale (CARS) and Autism Diagnostic Interview, Revised (ADI-R). Les paramètres d’intérêt englobaient les données sociodémographiques et cliniques ainsi que les principales comorbidités. La comparaison des proportions a été faite à l’aide test du chi-carré de Pearson. Des ANOVA à sens unique ont été calculées pour tester les différences de moyennes. Résultats. Au total, 120 enfants (sexe masculin 72,5 %) ont été examinés. Leur âge moyen au moment du diagnostic était de 7,83 ± 3,4 ans alors que les parents étaient alertés à 1,8 ±0,78 ans. Les retards de langage étaient le principal signe d'alerte (54 %) et le principal symptôme (62 %). Les troubles des interactions sociales (11,7 %) étaient sous rapportés par les parents. Les principaux signes étaient les troubles de l'interaction sociale (90,5 %), du comportement (80 %) et du langage (62,5 %). Les principaux symptômes des TSA étaient associés à l'épilepsie (p=0,027), à la paralysie cérébrale (p=0,026) et à une déficience auditive (p=0,045). Conclusion. Le profil clinique de l’autisme en RDC se caractérise par le retard de diagnostic et de langage en cooccurrence avec l'épilepsie et la déficience auditive. Le dépistage de l'autisme et ses principales comorbidités chez les enfants dans une approche multidisciplinaire sont prioritaires à Kinshasa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".