Effects of high frequency hearing loss on the University of Canterbury Adaptive Speech Test - Filtered Words (UCAST-FW)
Bibliographic record
Abstract
Objective: The primary purpose of this study was to determine the influence of high frequency peripheral hearing loss on test performance on the University of Canterbury Adaptive Speech Test – Filtered Words (UCAST-FW). We also aimed to investigate (1) if there is an ear advantage in performing the UCAST-FW; (2) whether there is any correlation between the UCAST-FW score and age; (3) the effectiveness of a binaural practice run in reducing the learning effect; and (4) the average time required for older adults to complete the UCAST-FW. Method: A total of 18 participants with normal hearing (≤ 25 dB Hearing Level (HL) at octave intervals of 250 through 4000 Hz in both ears) and 19 participants with varying degrees of high frequency sensorineural hearing loss (>25 dB HL at frequencies above 1000 Hz) were included in this study. All participants were native New Zealand English speakers, aged between 55-71 years, with normal low frequency hearing (≤25 dB at 250, 500 and 1000 Hz), had speech scores consistent with their audiogram, normal cognition and judged by the examiner to be capable of completing test protocols in terms of sufficient eyesight, alertness and motor control. Participants underwent a full diagnostic hearing test, the Montreal Cognitive Assessment, the Dichotic Digits Test, the Random Gap Detection Test and the UCAST-FW. Results: Findings indicated that the presence of a high frequency peripheral hearing loss had no significant influence on the UCAST-FW score. Findings also showed no significant ear advantage, or any trend between the participant’s UCAST-FW score and their age. A binaural practice run comprised of 5 initial and 15 working reversals was effective in reducing any learning effect. The UCAST-FW took an average of 15 minutes to complete, and the results showed no correlation between the test completion time and the participant’s age. Conclusion: Findings suggested that the listener’s high frequency peripheral hearing sensitivity had no significant influence on their UCAST-FW score and thus the UCAST-FW can potentially be an effective test for assessing Auditory Processing Disorder (APD) in older adults and the elderly regardless of their high frequency peripheral hearing sensitivity. The development of test material in New Zealand English, as well as the investigation of the validity of the UCAST-FW in assessing APD in older adults and the influence of cognitive functioning on test performance is necessary before the UCAST-FW can be implemented in New Zealand clinical Audiology settings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".