BRAIN GAMING EFFECTS ON MILD COGNITIVE IMPAIRMENT: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Objective: To systematically review and synthesize the research findings regarding the effects of brain gaming interventions on cognitive function of older adults with cognitive impairments (CI). Methods: A systematic search was conducted using PRISMA guidelines. A combination of key terms (i.e. brain gaming, older adult, dementia, cognition) were used to search for relevant literature on common electronic databases (MEDLINE, PubMed, EMBASE, CINAHL, PsycInfo, Cochrane Library Databases). Two level eligibility criteria were used to identify potential peer-reviewed manuscripts: 1) titles and abstracts were carefully reviewed by reviewers; and 2) full manuscripts that passed Level 1 review were retrieved for inclusion consideration. Frequencies, ranges, means and standard deviations were used to evaluate the studies characteristics and quality. Results: 766 studies were identified as potential for inclusion. 215 studies were excluded as duplicates. 515 abstracts were screened for Level 1 review accordingly to a priori criteria. 51 abstracts passed level 1 review and underwent full manuscript review (Level 2). Seven articles met full eligibility criteria for data extraction. Total sample of 396 older adults (77 yrs ± 6) with CIs including dementia were analyzed. Studies’ treatment ranged from 20 minutes session to 100 minute session. Primary outcomes were memory, learning, executing function, and visuospatial attention and depression as secondary. Five out of seven studies showed positive cognitive effects from the brain gaming interventions. Conclusion: Our results suggest that brain gaming has an overall positive effect on the cognitive function of older adults with CI. Although, high level of evidence-based research studies are recommended to reach conclusive results.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| 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) | 0.004 | 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".