How Does Cochlear Implantation Lead to Improvements on a Cognitive Screening Measure?
Bibliographic record
Abstract
Purpose Cognitive screening tools to identify patients at risk for cognitive deficits are frequently used by clinicians who work with aging populations in hearing health care. Although some studies show improvements in performance on cognitive screening exams when hearing loss intervention is provided in the form of a hearing aid or cochlear implant (CI), it is worth examining whether these improvements are attributable to increased auditory access to test items. This study aimed to examine whether performance and pass rate on a cognitive screening measure, the Montréal Cognitive Assessment (MoCA), improve as a result of CI, whether improved performance on auditory-based test items drives changes in MoCA performance, and whether postoperative MoCA performance relates to post-CI speech perception ability. Method Data were collected in adult CI candidates pre-implantation and 6 months postimplantation to examine the effect of intervention on MoCA performance. Participants were 77 CI users between the ages of 55 and 85 years. Participants completed the MoCA, administered audiovisually, and speech perception testing with monosyllabic (CNC) words at both intervals. Results Compared to 31 participants pre-operatively, 45 participants passed the MoCA postoperatively, which was a significant difference in pass rate. An improvement in MoCA scores could be attributed primarily to improvement in the "Delayed Recall" test domain, which was auditory based. Post-CI MoCA performance was related to post-CI CNC speech perception performance. Conclusions Improved performance and pass rates were demonstrated on the traditional MoCA test of cognitive screening from before to 6 months after CI. Improvements could primarily be attributed to better performance on a delayed recall task dependent on auditory access, and post-CI MoCA scores were related to post-CI speech perception abilities. Further studies are needed to investigate the application of cognitive screening tools in patients receiving hearing loss interventions, and these interventions' impact on patients' real-world functioning.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".