Association of Cognitive Impairment Screening Scores With Improvements in Speech Recognition and Quality of Life After Cochlear Implantation
Bibliographic record
Abstract
Importance: Many cochlear implant centers screen patients for cognitive impairment as part of the evaluation process, but the utility of these scores in predicting cochlear implant outcomes is unknown. Objective: To determine whether there is an association between cognitive impairment screening scores and cochlear implant outcomes. Design, Setting, and Participants: Retrospective case series of adult cochlear implant recipients who underwent preoperative cognitive impairment screening with the Montreal Cognitive Assessment (MoCA) from 2018 to 2020 with 1-year follow-up at a single tertiary cochlear implant center. Data analysis was performed on data from January 2018 through December 2021. Exposures: Cochlear implantation. Main Outcomes and Measures: Preoperative MoCA scores and mean (SD) improvement (aided preoperative to 12-month postoperative) in Consonant-Nucleus-Consonant phonemes (CNCp) and words (CNCw), AzBio sentences in quiet (AzBio Quiet), and Cochlear Implant Quality of Life-35 (CIQOL-35) Profile domain and global scores. Results: A total of 52 patients were included, 27 (52%) of whom were male and 46 (88%) were White; mean (SD) age at implantation was 68.2 (13.3) years. Twenty-three (44%) had MoCA scores suggesting mild and 1 (2%) had scores suggesting moderate cognitive impairment. None had been previously diagnosed with cognitive impairment. There were small to medium effects of the association between 12-month postoperative improvement in speech recognition measures and screening positive or not for cognitive impairment (CNCw mean [SD]: 48.4 [21.9] vs 38.5 [26.6] [d = -0.43 (95% CI, -1.02 to 0.16)]; AzBio Quiet mean [SD]: 47.5 [34.3] vs 44.7 [33.1] [d = -0.08 (95% CI, -0.64 to 0.47)]). Similarly, small to large effects of the associations between 12-month postoperative change in CIQOL-35 scores and screening positive or not for cognitive impairment were found (global: d = 0.32 [95% CI, -0.59 to 1.23]; communication: d = 0.62 [95% CI, -0.31 to 1.54]; emotional: d = 0.26 [95% CI, -0.66 to 1.16]; entertainment: d = -0.005 [95% CI, -0.91 to 0.9]; environmental: d = -0.92 [95% CI, -1.86 to 0.46]; listening effort: d = -0.79 [95% CI, -1.65 to 0.22]; social: d = -0.51 [95% CI, -1.43 to 0.42]). Conclusions and Relevance: In this case series, screening scores were not associated with the degree of improvement of speech recognition or patient-reported outcome measures after cochlear implantation. Given the prevalence of screening positive for cognitive impairment before cochlear implantation, preoperative screening can be useful for early identification of potential cognitive decline. These findings support that screening scores may have a limited role in preoperative counseling of outcomes and should not be used to limit candidacy.
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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 | 0.006 |
| 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 | 0.000 | 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".