The Inter-Dependence Between Cognitive Impairement And Functional Mobility With Risk Of Fall In Older Individuals.
Bibliographic record
Abstract
Background: Ageing is the natural developmental process where there are deterioration and loss of function over time. This study shows the comparison between cognition impairment and functional mobility with the risk of fall in healthy older individual and results shows that there is a connection between cognitive impairment and increasing age, but it has nothing to do with the risk of fall and cognitive impairment. Objective: The study aims to assess the cognitive impairment, functional mobility, and risk of fall in a correlation of cognitive impairment and functional mobility. Method: In this study, 50 subjects were selected according to the inclusion criteria at Jalgaon city. The study duration was six months, and it was done based on the Geriatric Depression Scale, Montreal Cognitive Assessment, Peninsula Health and Fall Risk Assessment Tool, Timed Up and Go test. Result: According to the study, if the TUG test's time is more than the chances of falls are more. The scoring of MoCA and FRAT are interrelated such as, if the score of MoCA is less than expected, then chances of falls are higher compared to an average score. There is a significant negative correlation of MoCA and FRAT as the p-value is p=0.02, and positive correlation of FRAT and TUG as the p-value is p=0.003, which is significant. Conclusion: Study has concluded that, as the age increases, there is an impact on cognitive ability which affects the functional mobility and increases the chances of fall in the elderly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".