Changes in cognitive performance after cochlear implantation in adults and older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives To conduct critical assessment of the literature on the effects of cochlear implantation on adults’ cognitive abilities.Design PubMed, Scopus, Lilacs, Web of Science, Livivo, Cochrane, Embase, PsycInfo, and grey literature were searched. Eligibility criteria: age 18 or over with severe-to-profound bilateral hearing loss, cochlear implantation, cognitive test before and after implantation. Risk of bias was assessed using ROB, ROBINS-I and MASTARI tools. Meta-analysis was performed.Study sample Out of 1830 studies, 16 met the inclusion criteria.Results On AlaCog test, significant improvement was found after implantation [MD = −46.64; CI95% = −69.96 to −23.33; I2 = 71%]. No significant differences were found on the Flanker, Recall, Trail A and n-back tests (p > 0.05). For MMSE, no significance was found [MD 0.63; CI 95% = −2.19 to 3.45; I2 = 88%]. On TMT, an overall significant effect with a 9-second decrease in processing speed post-implantation [MD = −9.43; CI95% = −15.42 to −3.44; I2 = 0%].Conclusion Cognitive improvements after cochlear implantation may depend on time and the cognitive task evaluated. Well-designed studies with longer follow-up are necessary to examine whether cochlear implantation has a positive influence on cognitive abilities. Development of cognitive assessment tools to hearing-impaired individuals is needed.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".