Predicting Hearing aid Benefit Using Speech-Evoked Envelope Following Responses in Children With Hearing Loss
Bibliographic record
Abstract
Electroencephalography could serve as an objective tool to evaluate hearing aid benefit in infants who are developmentally unable to participate in hearing tests. We investigated whether speech-evoked envelope following responses (EFRs), a type of electroencephalography-based measure, could predict improved audibility with the use of a hearing aid in children with mild-to-severe permanent, mainly sensorineural, hearing loss. In 18 children, EFRs were elicited by six male-spoken band-limited phonemic stimuli--the first formants of /u/ and /i/, the second and higher formants of /u/ and /i/, and the fricatives /s/ and /∫/--presented together as /su∫i/. EFRs were recorded between the vertex and nape, when /su∫i/ was presented at 55, 65, and 75 dB SPL using insert earphones in unaided conditions and individually fit hearing aids in aided conditions. EFR amplitude and detectability improved with the use of a hearing aid, and the degree of improvement in EFR amplitude was dependent on the extent of change in behavioral thresholds between unaided and aided conditions. EFR detectability was primarily influenced by audibility; higher sensation level stimuli had an increased probability of detection. Overall EFR sensitivity in predicting audibility was significantly higher in aided (82.1%) than unaided conditions (66.5%) and did not vary as a function of stimulus or frequency. EFR specificity in ascertaining inaudibility was 90.8%. Aided improvement in EFR detectability was a significant predictor of hearing aid-facilitated change in speech discrimination accuracy. Results suggest that speech-evoked EFRs could be a useful objective tool in predicting hearing aid benefit in children with hearing loss.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".