Greater working memory and speech perception scores in cochlear implant users predict better subjective quality of life and hearing
Bibliographic record
Abstract
Abstract A common concern in individuals with cochlear implants (CIs) is difficulty following conversations in noisy environments and social settings. The ability to accomplish these listening tasks relies on the individual’s working memory abilities and draws upon limited cognitive resources to accomplish successful listening. For some individuals, allocating too much, can result deficits in speech perception and in long term detriments of quality of life. For this study, 31 CI users and NH controls completed a series of online behavioural tests and quality of life surveys, in order to investigate the relationship between visual and auditory working memory, clinical and behavioural measures of speech perception and quality of life and hearing. Results showed NH individuals were superior on auditory working memory and survey outcomes. In CI users, recall performance on the three working memory span tests declined from visual reading span to auditory listening in quiet and then listening in noise and speech perception was predictably worse when presented with noise maskers. Bilateral users performed better on each task compared to unilateral/HA and unilateral only users and reported better survey outcomes. Correlation analysis revealed that memory recall and speech perception ability were significantly correlated with sections of CIQOL and SSQ surveys along with clinical speech perception scores in CI users. These results confirm that hearing condition can predict working memory and speech perception and that working memory ability and speech perception, in turn, predict quality of life. Importantly, we demonstrate that online testing can be used as a tool to assess hearing, cognition, and quality of life in CI users.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".