Cognitive Impairment in Older Adults With Concurrent Hearing and Vision Impairment: A Systematic Scoping Review Protocol
Bibliographic record
Abstract
Introduction: As the prevalence of age-related sensory impairment increases, more evidence emerges on the association between uni-sensory and cognitive impairment (CI) in older adults. However, the link between CI and concurrent hearing and vision impairment (referred to as dual sensory impairment/DSI) is not well-understood, and this combined effect may be additive or multiplicative. Moreover, the existing evidence on CI in older adults with DSI is scattered and limited. Through this systematic scoping review, we aim to map existing evidence on CI in older adults with DSI, and to summarize what is known about the prevalence, incidence and risk factors of CI, and tools used to screen or assess CI in older adults with DSI. Methods and Analysis: We will use the Joanna Briggs Institute framework to perform the review. Eleven databases [MEDLINE, CINAHL/EBSCO, EMBASE, Mednar, WorldWideScience, PsycEXTRA, OAIster, OpenGrey (SIGLE), Global Health, PsycINFO, and Web of Science] and clinical trial registries (ISRCTN Registry, WHO ICTRP, and ClinicalTrials.gov ) will be searched. Study selection will be completed using Covidence, and data will be extracted using an a priori data extraction tool. To be included, studies had to be peer-reviewed, had older adults with DSI as the focal population, and are related to CI. Data will be presented using a narrative summary with emphasis on implications for future research and practice. Discussion: Reliable cognitive screening is of the utmost importance for prevention and treatment of CI within DSI population. The study findings will have significant implications for health services delivery and policy research. The summarized findings on the prevalence, incidence, associated risk factors, and CI screening and assessment tools will inform geriatric care. The review will also document knowledge gaps on CI in the DSI population and identify areas of interest for future studies. Ethics and Dissemination: The scoping study, being a review of existing documents, does not require ethics approval. The findings will be disseminated with relevant stakeholders using knowledge translation activities such as scientific presentations and publications. We intend to use the findings to conduct a Delphi study to evaluate which CI tools are suitable for older population 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 | 0.064 | 0.065 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.007 |
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".