Brain Games for Dementia: Do They Help?
Bibliographic record
Abstract
Abstract Brain Gaming (BG) Interventions have been shown to improve the cognitive function of older adults with cognitive impairments (CIs). However, rigorous evaluation supporting BG effectiveness is needed. Thus, we used meta-analysis to evaluate the effectiveness of BG. Several search databases (i.e. Pubmed) were used to identify relevant randomized controlled trials (RCTs). Cochrane RoB tool evaluated risk of bias. The main outcome was the composite score of cognitive function. Inverse-variance random effects model was used to compare the pooled standardized mean difference (SMD) across studies. A total of 16 RCTs included 909 participants. The RCTs varied in sample size, gaming platform, training prescription, and cognition. The meta-analysis showed no significant effects of BG on overall cognitive function (pooled SMD = 0.08, 95% CI [-0.24 – 0.41], p = 0.61, I2 = 77%. However, due to high heterogeneity, we cannot confidently refute that BG is an effective cognitive training approach.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".