Cognitive Screening of Adults With Postlingual Hearing Loss: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the evidence for the use of cognitive screening tools for adults with postlingual hearing loss. DATA SOURCES: PubMed, Embase, Scopus, PsycINFO (EBSCO), CINAHL (EBSCO), and CENTRAL (Cochrane Library) electronic databases were searched from inception until October 4, 2018. REVIEW METHODS: Articles were reviewed for inclusion by 2 independent reviewers. The references of included articles were hand-searched for additional relevant articles. Data were extracted by 2 independent extractors. RESULTS: Of 2092 articles imported from the search, 81 were included for the review. Nearly a third (31%, n = 25) included patients with profound hearing loss. In total, 23 unique tools were used for 105 unique applications. The Mini Mental Status Exam (MMSE) was the most commonly used (54%, n = 55), followed by the Montreal Cognitive Assessment (MoCA) (19%, n = 10). Nearly half of the tools were used to define patient inclusion or exclusion in a study (48%, n = 50), followed by examination of a change after an intervention (26%, n = 27). Two articles attempted to study the validity of the MMSE and MoCA for screening patients with mild to moderate hearing loss and found mixed effects of the auditory components. There were no validation studies identified from the search. CONCLUSION: Many different cognitive screening tools have been used to study patients with postlingual hearing loss. The effects of the auditory components of these tools may be deleterious but ultimately remain unclear from the available evidence. To date, there has been no validation of any cognitive screening tool to be used for adults with postlingual hearing loss.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".