Bibliographic record
Abstract
Introduction: Falls are the leading cause of injury in older adults, resulting in worsening comorbidities, bone fractures, post-fall syndrome, and soft tissue injuries. This retrospective study aims to investigate the association between fall risk and the Montreal Cognitive Assessment (MoCA) scores, as well as other risk factors, among older adults in Thailand. Methods: Data on patients who experienced a fall within a 12-month period were collected from medical records at the Geriatric Excellence Center, King Chulalongkorn Memorial Hospital. Potential fall risk factors available in the medical records included ability to perform activities of daily living, level of dependency, depression, cognitive function, nutritional status, use of sleep medications, nocturia, handgrip strength, and the presence of sarcopenia according to the Asian Working Group for Sarcopenia criteria. Additionally, information regarding the use of various medications, particularly sleeping pills, was also recorded. Univariate and multiple regression analysis was conducted to assess the impact of different factors. Results: A total of 5336 older adults were included in this study, of which 4309 were female (75.83%) and 1,027 were male (24.17%). The mean age was 68.3 (±5.0) years (age range from 60 to 96 years). Multiple logistic regression analysis revealed that age, being female, cognitive impairment, low handgrip strength, gait speed, use of sleep medications, and urinary incontinence were associated with an increased incidence of falls. Among Thai older adults, the slow gait speed showed the strongest association with falls risk. Conclusion: Risk factors for falls among older adults in Thailand include age, being female, cognitive impairment, low handgrip strength, gait speed, use of sleep medications, and urinary incontinence. These findings are consistent with research conducted in other populations, emphasizing the need for comprehensive assessments and targeted interventions to address these factors and prevent falls in at-risk individuals.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".