The Effect of Brain Training Game activities on Improvement of Cognitive Function measured by Montreal Cognitive Assesment Indonesia version \n(MoCA-Ina)
Bibliographic record
Abstract
Background: The function of cognition in young adults (around the age of 20 years) mostly does not \ndevelop to its peak, even though at that age a person needs better cognitive abilities to deal with the lectures. \nBrain training game activities by playing games through the NeuronationTM application can improve cognitive \nfunction. \nObjective: To determine the effect of Brain training activities on improvement of cognitive function \nMethod Experimental study with two group pre and post design. 74 Sample were medical students of \nFaculty of Medicine, University of Muhammadiyah Malang class of 2017-2018, divided into control and \ntreatment groups of 37 samples each. The treatment group was given by game brain game training 30 minutes \na day, 20 times in 4 weeks.. Cognitive function was measured by the MoCA-Ina test in the control group and \npre and post test. The hypothesis test used the pairet samples test. \nResult: Cognitive function of pre and post test of the control group good cognitive function increased 2.69% \nand cognitive function of pre and post test of treatment group increased 58.8% with the result of pairet \nsample analysis of significant p = 0,000 which means that in the control group there was a tendency for \nincreased function cognition after treatment (post test) but the improvement was not significant. In the \ntreatment group with brain training activities cognitive function increased sharply and statistically significant, \nConclusion: Brain training activities affect the improvement of cognitive function
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".