The Observation Scale for Autism (OSA): A New Screening Method to Detect Autism Spectrum Disorder before Age Three Years
Bibliographic record
Abstract
Background: There is an increasing body of evidence that early interventions for children with Autism Spectrum Disorder (ASD) promote a positive development of social interaction. Thus, tools for early detection of ASD are warranted. Aim: Development of, and deciding cut-off-levels for, a new screening tool for ASD, the Observation Scale for Autism (OSA). The OSA was designed to be used at the free health check-up at 30 months, offered to all children in Sweden. Method: The OSA consists of 12 observations and takes less than 10 minutes to use. The performance of the test was investigated by assessing 37 children previously diagnosed ASD, 23 with Down Syndrome (DS) and 26 typically developing children (TD). Results: Children diagnosed with ASD showed statistically significant higher scores in all 12 items compared to TD children, and significantly higher in 10 items compared to the children with DS. Most of the observations in OSA seemed to cover specific symptoms of ASD, but two of the observations were more related to developmental level. The nine most discriminative items for ASD were identified, and among those, a cut-off limit was chosen (≥3 items). Among children with ASD, 34/37 reached the proposed cut off, compared to 0/26 and 4/23 among children in the TD and DS groups, respectively. Conclusion: The results suggest that the OSA discriminates children with ASD from TD children and children with DS. Using the suggested cut off, OSA provides high sensitivity for ASD (92%) with a very low false positive rate.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".