Screening for Autism in <scp>M</scp>exico
Bibliographic record
Abstract
In order to conduct the screening phase of the first epidemiological survey of autism spectrum disorders (ASDs) in Mexico, we needed a screening tool to detect autistic symptomatology in a large sample of school-age children. We used the Spanish version of the Social Responsiveness Scale (SRS). We recruited a clinical sample of 200 children (81% males; mean age: 7.4 years) with a confirmed diagnosis of ASDs and a sample of 363 control children (59.5% males; mean age: 8.5 years) without ASDs. Three-way analyses of variance (ANOVAs) identified a main effect of clinical status (ASDs vs. controls) for both parent and teacher scales, but no gender or age effect. The mean total and subscale raw scores were significantly different between the clinical and control groups for the parent and for the teacher SRS (P < 0.001). The internal consistency of the SRS was excellent. Receiver operating characteristic (ROC) analyses showed excellent discriminant validity of the SRS in the Mexican sample (area under the curve: 0.962 for the parent, 0.960 for the teacher). ROC curves were also used to determine which cutoff would provide the best trade-off between sensitivity and specificity. Mexican SRS scores were significantly higher than in the U.S. and German population for typically developing children but comparable for clinically referred subjects. The SRS is an acceptable screening instrument for epidemiological studies of ASDs in Mexico. Its psychometric properties are excellent and comparable to those derived from North American and other samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".