ASSOCIATION BETWEEN COGNITIVE FUNCTION WITH RISK OF FALL IN ELDERLY PATIENTS
Bibliographic record
Abstract
Background: Fall is a problem that often occurs in the elderly. The incidence of falls continues to increase from middle age and peaks at the age of more than 80 years. The risk of fall is closely related to cognitive impairment in the elderly. Impaired cognitive function can result in postural instability and increase the risk of falls. Impaired cognitive function causes poor judgment and decision making, impaired reaction, attention and speed of information processing. Objectives: To determine the relationship of cognitive function with the risk of fall in elderly patients. Research Methods: This study is a cross-sectional study with consecutive sampling techniques, where elderly people over 60 years who go to neurology and geriatric polyclinics who meet the exclusion and inclusion criteria are included in this study. The study was taken as many as 51 subjects consecutively. Data analysis using fisher exact test and pearson correlation test. Result of the study: Demographic characteristics of research subjects are the most age range at age 60-69 years, female sex, high school education level, housewife occupation. The average MoCA INA score was 22,82 ± 3,99 and the balance scale berg score was 46.29 ± 6.62. The abnormal MoCA INA score is more for respondents with moderate fall risk, namely 14 people (38,9%) and there was a relationship between cognitive function and risk of fall with p = 0.000 (p <0.001) with a positive correlation direction with strong correlation strength (r = 0.679). Conclusion: there is a significant relationship between cognitive function and the risk 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 | 0.002 |
| 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) | 0.002 | 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".