Examination of characteristics and management of children with hearing loss and autism spectrum disorders
Bibliographic record
Abstract
OBJECTIVE: Up to 40% of children with hearing loss present with other developmental disabilities. The purpose of this study was to document the prevalence of autism spectrum disorders (ASD) in children with permanent hearing loss, to describe the audiologic characteristics, and to examine clinical management. DESIGN: Prospective data related to clinical characteristics of children identified with hearing loss and ASD were examined. A retrospective chart review was also conducted to explore clinical management and uptake of amplification. STUDY SAMPLE: The study included all children in one Canadian region identified with permanent hearing loss and followed from 2002-2010. RESULTS: Of a total of 785 children with permanent hearing loss, 2.2% (n = 17) also received a diagnosis of ASD. The 13 boys and 4 girls presented with a range of audiologic profiles from unilateral to profound bilateral hearing loss. Four of five children with unilateral hearing loss experienced progression to bilateral loss. Amplification was recommended for all but one child and 9 of 16 children continued to use their hearing devices. CONCLUSIONS: The higher prevalence rate of ASD in this clinical population is consistent with previous reports. Our findings suggest that some children with autism can derive benefits from the use of amplification.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".