Hearing‐impaired population performance and the effect of hearing interventions on Montreal Cognitive Assessment (<scp>MoCA</scp>): Systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Older adults are at high risk of developing age-related hearing loss (HL) and/or cognitive impairment. However, cognitive screening tools rely on oral administration of instructions and stimuli that may be impacted by HL. This systematic review aims to investigate (a) whether people with HL perform worse than those without HL on the Montreal Cognitive Assessment (MoCA), a widely used screening tool for cognitive impairment, and what the effect size of that difference is (b) whether HL treatment mitigates the impact of HL. METHOD: We conducted a systematic review and meta-analysis including studies that reported mean MoCA scores and SDs for individuals with HL. RESULTS: People with HL performed significantly worse on the MoCA (4 studies, N = 533) with a pooled mean difference of -1.66 points (95% confidence interval CI -2.74 to -0.58). There was no significant difference in MoCA score between the pre- vs post-hearing intervention (3 studies, N = 75). However, sensitivity analysis in the cochlear implant studies (2 studies, N = 33) showed improvement of the MoCA score by 1.73 (95% CI 0.18 to 3.28). CONCLUSION: People with HL score significantly lower than individuals with normal hearing on the standard orally administered MoCA. Clinicians should consider listening conditions when administering the MoCA and report the hearing status of the tested individuals, if known, taking this into account in interpretation or make note of any hearing difficulty during consultations which may warrant onward referral. Cochlear implants may improve the MoCA score of individuals with HL, and more evidence is required on other treatments. J Am Geriatr Soc 68:-, 2020.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".