Comparison of Different Hearing Aid Prescriptions for Children
Bibliographic record
Abstract
OBJECTIVES: To assess whether there are significant differences between speech scores for different hearing aid prescription methods, specifically DSL i/o, DSL V, and NAL-NL1, using age-appropriate closed- and open-set speech tests with young children, designed to avoid floor and ceiling effects. DESIGN: Participants were 44 children with moderate or severe bilateral hearing loss, 8 aged 2 to 3 years, 15 aged 4 to 5 years, and 21 aged 6 to 9 years. Children wore bilateral hearing aids fitted with each prescription method in turn in a balanced double-blind design. The speech tests used with each child (and for some tests the levels) were chosen so as to avoid floor and ceiling effects. For the closed-set tests, the level used was selected for each child based on their hearing loss. The tests used were: (1) The closed-set Consonant Confusion Test of word identification; (2) The closed-set Chear Auditory Perception Test (CAPT) of word identification. This has separate sections assessing discrimination of consonants and vowels and detection of consonants; (3) The open-set Cambridge Auditory Word Lists for testing word identification at levels of 50 and 65 dBA, utilizing 10 consonant-vowel-consonant real words that are likely to be familiar to children aged 3 years or older; (4) The open-set Common Phrases Test to measure the speech reception threshold in quiet; (5) Measurement of the levels required for identification of the Ling 5 sounds, using a recording of the sounds made at the University of Western Ontario. RESULTS: Scores for the Consonant Confusion Test and CAPT consonant discrimination and consonant detection were lower for the NAL-NL1 prescription than for the DSL prescriptions. Scores for the CAPT vowel-in-noise discrimination test were higher for DSL V than for either of the other prescriptions. Scores for the Cambridge Auditory Word Lists did not differ across prescriptions for the level of 65 dBA, but were lower for the NAL-NL1 prescription than for either of the DSL prescriptions for the level of 50 dBA. The speech reception threshold measured using the Common Phrases Test and the levels required for identification of the Ling 5 sounds were higher (worse) for the NAL-NL1 prescription than for the DSL prescriptions. CONCLUSIONS: The higher gains prescribed by the DSL i/o and DSL V prescription methods relative to NAL-NL1 led to significantly better detection and discrimination of low-level speech sounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".