Greater working memory in cochlear implant users is related to higher subjective quality of life
Bibliographic record
Abstract
Introduction A common concern for individuals with moderate-to-profound hearing loss who have received a cochlear implants (CIs) is difficulty following conversations in noisy environments. A possible reason is the increased use of cognitive resources (attention and working memory) to disambiguate the speech stimuli potentially causing detriments in functional aspects of life. However, this relationship is not well-documented. The overall arching goal of this study was to quantify the relationships between Patient-Reported Outcome Measures (PROMs) and cognitive ability through working memory and speech perception measures. Methods In the current study, 31 adult CI users and typical hearing (TH) age-matched controls were recruited. CI users completed PROMs including Speech, Spatial and Quality of hearing survey (SSQ) and versions of a quality of life (QOL) for CI users (CIQOL) and Nijmegen Cochlear Implant Questionnaire (NCIQ). Measures of cognition were assessed using internet-based tools. Outcomes were compared between groups and PROMs and were related to the cognitive tasks. Results Reduced auditory working memory and speech perception in noise were observed in CI users compared to TH controls. Correlation analyses revealed significant domain-specific PROM relationships with cognitive tasks in CI users, but not in TH controls. The SSQ had more significant relationships compared to the other PROMS. Conclusion These results suggest that internet-based metrics of cognitive ability are associated with the functional hearing aspects of life in CI users and that SSQ is more sensitive to investigate the cognitive related effects of QOL compared to other commonly used PROMs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".