IDENTIFICATION OF COGNITION LEVEL IN PHYSICALLY ACTIVE AND INACTIVE YOUNG ADULTS
Bibliographic record
Abstract
Background Cognition is basically a mental act or process of obtaining knowledge and understanding idea, experience, and the senses. Cognitive impairment includes memory impairment observed by some other person but these patients have normal general cognitive functions and have no issues in daily life works, also there is memory impairment with respect to age and education and there is no dementia. Objective: To show the relation between physical activity and cognition in young adults. This study also aims to develop a strong connection between physical activity and cognition so physical activity can be used as a tool for improving cognition. Methods Based on inclusion and exclusion criteria, a convenience sample of 137 participants was chosen. Participants ranged in age from 18 to 40 years old. A self-administered questionnaire was circulated among participants and MOCA (MONTREAL COGNITIVE ASSESMENT) was used to analyze the cognitive function. The data was analyzed using SPSS version 23. Results Total of 137 participants were included in this study. Frequency of physically inactive young adults was 51, out of these 27 participants scored less than 26 and showed declined cognition while 24 participants scored 26 or more and showed normal cognition. Frequency of physically active young adults was 86, out of these 15 scores less than 26 and showed declined cognition while 71 participants scored 26 or more and showed normal cognition. Conclusion It was concluded that physically inactive participants showed more declined in level of cognition then physically active individuals which showed a high level of cognition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".