Use of the autism spectrum screening questionnaire for identification of autism spectrum disorders in 8-10 years old georgian children*
Bibliographic record
Abstract
Introduction Rising prevalence of autism spectrum disorders highlights importance of research priority of development of effective screening procedures for schoolage children. Objectives The study aimed to identify the prevalence of ASD among 8-10 y old schoolchildren in Republic of Georgia. Methods In 2019 a cross sectional survey in four main cities of Republic of Georgia was conducted, totally 3rd and 4th grade (8-10 y old) 16654 children from 211 public schools were included. The Autism Spectrum Screening Questionnaire (ASSQ), completed by parents and teachers, was used to determine children at risk for ASD. Results 16654 (response rate 74%) parents were agreed to participate in the study. Parents and teachers rated 770 (5.0%) and 669 children (4.9%), respectively, as screen positive (in top five percentile). Cut-off scores for 99-95 percentiles (top 1-5%) was defined. Boys were more likely to be rated screenpositive than girls. Share of boys rated in the top 5% by parents is 5.6% compared to 4.3% of girls. Teachers place boys in the top 5% even more frequently – 6.4% versus 3.4% girls. Pairwise correlation coefficients (0.53) revealed moderate correlations between scores and according to p-values (< 0.05) all correlations were statistically significant. Conclusions The study defined the cut-off scores of ASSQ for 8-10 y old Georgian children and gender difference in prevalence of risk for ASD. Using the ASSQ was an effective instrument and could be used in school settings to identify children with special needs. *This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNSFG), grant - FR-18-304. Disclosure No significant relationships.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".