Auditory, visual and cognitive abilities in relation to cochlear implant outcome in elderly
Bibliographic record
Abstract
Introduction: Cochlear implantation (CI) is the standard-of-care for individuals with severe to profound hearing loss. Nowadays, there is an increase in the number of older patients among the post-lingual hearing-impaired candidates for CI. Within this elderly population, a large variation in the degree of CI benefit has been reported, especially pertaining to speech understanding. It is suggested that the variation in speech understanding may not solely be due to peripheral auditory factors. Speech understanding is considered a multisensory process, whereby visual information (e.g. from mouth movements) is integrated with auditory information in order to increase intelligibility [1]. Besides visual information, also cognitive functions (i.e. top-down processes) are involved in speech processing. More specific, working memory, processing speed, selective attention, as well as cognitive flexibility and inhibition are required for speech processing [2], especially in unfavorable listening conditions (e.g. background noise, hearing impairment or listening through a CI). Therefore, the aim of the current study was to identify various factors, including auditory, visual and cognitive factors, predicting CI outcome in elderly CI users. Methods: Five elderly CI users with a severe to profound post-lingually acquired hearing loss were included in this study. Age ranged from 71 to 79 years (mean 76.0 years, standard deviation 3.60). For all participants, auditory, visual and cognitive abilities were investigated behaviorally and subjectively. The auditory test battery consisted of pure-tone audiometry, speech audiometry in quiet and in noise. The visual speech processing abilities were evaluated using the Test for (Audio-)Visual Speech Perception (TAUVIS) [3]. For evaluating the cognitive abilities, all participants were first screened for mild cognitive impairment using the Montreal Cognitive Assessment (MoCA) [4]. Besides, the subtest ‘Repeating Digits and Letters’ from the WAIS IV-NL [5] was used to measure working memory capacity and processing speed. Selective attention, and cognitive flexibility and inhibition, were investigated using the subtest ‘Letter Detection’ from the Cognitive Test Battery for Seniors [6] and an auditory Stroop test [7], respectively. The subjective impact of hearing loss on quality of life was investigated using the hearing-related quality of life questionnaire for Auditory-VIsual, COgnitive and Psychosocial functioning (hAVICOP) [8]. The contribution of the auditory, visual and cognitive abilities to speech understanding in quiet and in noise will be investigated using linear regression analyses. Results and conclusions: This study aimed to identify the contribution of auditory, visual and cognitive factors, to CI outcome in elderly. It is hypothesized that specifically the contribution of the cognitive abilities could be responsible for the variation in speech understanding outcome in elderly CI users. Currently, data collection is still ongoing, and the results will be presented at the HeAL conference 2022.
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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 | 0.001 |
| 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) | 0.002 | 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".