Experience with the Rapid Interactive Test for Autism in Toddlers in an Autism Spectrum Disorder Diagnostic Clinic
Bibliographic record
Abstract
OBJECTIVE: To examine the psychometric properties of the Rapid Interactive Screening Test for Autism in Toddlers (RITA-T) in an autism spectrum disorder (ASD) clinic for children aged 18 to 36 months. METHODS: The RITA-T (level 2 screening instrument) was integrated into an ASD screening and diagnostic process for evaluating children aged 18 to 36 months who were referred to a pediatric tertiary care center. Scoring of the RITA-T to differentiate ASD from non-ASD developmental concerns was evaluated. Screening instrument measurements included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), and negative likelihood ratio (LR-). RESULTS: From a total of 239 participants aged 18 to 36 months (males = 78% and females = 22%), 201 (84%) were diagnosed with ASD (4:1 male-to-female ratio). An ASD diagnosis was significantly associated with RITA-T scores, with ASD patients scoring higher than non-ASD patients [F (1,235) = 170, mean difference: males 9.21, mean difference: females 12.4, p < 0.001]. The RITA-T score was not statistically correlated with age or sex. The optimal cutoff score of ≥14 was determined from a receiver operator curve analysis (area under the curve = 0.953). In the study group, with a cutoff score of ≥14, the RITA-T showed a sensitivity of 0.97, specificity of 0.71, PPV of 0.95, NPV of 0.79, LR+ of 3.33, and LR- of 0.05. CONCLUSION: The RITA-T, as a level 2 screening instrument for ASD, exhibits discriminative psychometric properties similar to previously published results. When integrated into an ASD screening and diagnostic process for families for whom concerns about ASD have been raised with their children aged 18 to 36 months, the RITA-T helps to predict a best-estimate clinical diagnosis of ASD.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".