Risk factors and association of cognitive impairment among older adults with concurrent vision and hearing impairment: A scoping study
Bibliographic record
Abstract
Abstract Background The concurrent loss of vision and hearing, termed dual sensory impairment (DSI), can have a significant impact on one’s functioning. Evidence is emerging on the prevalence of DSI in the older population, including those with cognitive impairment (CI). In the context of an ageing population with increasing prevalence of DSI, we aimed to synthesize existing evidence on the association of CI with DSI and identify risk factors for the development of CI in older adults with DSI, establishing its relevance to primary prevention. Method The scoping review was performed using Arskey and O’Malley’s framework (2005). In June 2020, eleven scientific databases (CINAHL, Embase, Global Health, Mednar, OAIster, OpenGrey, PsycEXTRA, PsycINFO, PubMed, Web of Science, and WorldWideScience) were searched using index terms and keywords relating to CI and DSI. To be included, studies had to be empirical, peer‐reviewed, had older adults with DSI as the focal population, and have explored the concept of CI as an impairment of one or more cognitive domains, such as memory, attention, or executive functions. The screening of the retrieved studies was completed using Covidence, and relevant data were extracted using an a priori data extraction tool. Result Out of 11,595 identified sources, 56 articles were included. Twelve studies reported the prevalence of CI in older adults with DSI, while 20 reported risk factors for CI in older adults that could be applied to older adults with DSI as well, such as depression, hearing impairment, and social isolation. Thirteen studies presented DSI alone as a risk factor for CI in older people. Seven studies reported other risk factors for CI in older adults with DSI, such as non‐sensory comorbidities, illiteracy, physical inactivity, and gender. Conclusion Our review indicates that further population‐based longitudinal research is needed to explore the association between CI and DSI and identify whether there are risk factors that are unique for CI among older adults with DSI. With the prevalence of DSI increasing in the older population, robust evidence is needed to inform the recommendations regarding screening, assessment and management of CI in older adults with DSI.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".