Sensitivity and specificity of early screening for autism
Bibliographic record
Abstract
BACKGROUND: Early identification and diagnosis is beneficial for children with autism spectrum disorder (ASD). Universal early screening is recommended by many experts, but disputed because evidence is limited, and sensitivity and specificity in general populations are largely unknown. AIMS: To estimate the sensitivity and specificity of early population-based screening for ASDs. METHOD: The study was based on the Norwegian Mother and Child Cohort Study. The 36-month cohort questionnaire included the Social Communication Questionnaire (SCQ), a 40-item screening instrument for ASD. RESULTS: A total of 58 520 mothers (58%) responded to the questionnaire. By the end of follow-up on 31 December 2015, 385 (0.7%) individuals with ASD had been identified among the responders' children. The distributions of SCQ scores in those with ASD and other children had large degrees of overlap. With the cut-off of 15 recommended in the SCQ manual, screening sensitivity was 20% (95% CI 16-24) for ASD overall. For children with ASD who had not developed phrase speech at 36 months, sensitivity was 46% (95% CI 35-57%), whereas it was 13% (95% CI 9-17) for children with ASD with phrase speech. Screening specificity was 99% (95% CI 99-99). With the currently recommended cut-off of 11, sensitivity increased to 42% for ASD overall (95% CI 37-47), 69% (95% CI 58-79) for ASD without phrase speech and 34% (95% CI 29-40) for ASD with phrase speech. Specificity was then reduced to 89% (95% CI 89-90). CONCLUSIONS: Early ASD screening with a parent checklist had low sensitivity. It identified mainly individuals with ASD with significant developmental delay and captured very few children with ASD with cognitive skills in the normal range. Increasing sensitivity was not possible without severely compromising specificity. DECLARATION OF INTEREST: C.L. receives royalty for the Social Communication Questionnaire, which she has co-authored.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".