Appraising the need for audiological assessment before autism spectrum disorder referral
Bibliographic record
Abstract
Objectives: Mandatory audiological testing before autism spectrum disorder (ASD) assessment is common practice. Hearing impairment (HI) in the general paediatric population is estimated at 3%; however, hearing impairment prevalence among children with ASD is poorly established. Our objective was to determine which children referred for ASD assessment require preliminary audiological assessment. Methods: Retrospective chart review of children (n=4,173; 0 to 19 years) referred to British Columbia's Autism Assessment Network (2010 to 2014). We analyzed HI rate, risk factors, and timing of HI diagnosis relative to ASD referral. Results: ASD was diagnosed in 53.4%. HI rates among ASD referrals was 3.3% and not significantly higher in children with ASD (ASD+; 3.5%) versus No-ASD (3.0%). No significant differences in HI severity or type were found, but more ASD+ females (5.5%) than ASD+ males (3.1%) had HI (P<0.05). Six HI risk factors were significant (problems with intellect, language, vision/eye, ear, genetic abnormalities, and prematurity) and HI was associated with more risk factors (P<0.01). Only 12 children (8.9%) were diagnosed with HI after ASD referral; all males 6 years or younger and only one had no risk factors. ASD+ children with HI were older at ASD referral than No-ASD (P<0.05). Conclusions: Children with ASD have similar hearing impairment rates to those without ASD. HI may delay referral for ASD assessment. As most children were diagnosed with HI before ASD referral or had at least one risk factor, we suggest that routine testing for HI among ASD referrals should only be required for children with risk factors.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".