Why Frail Older Adults Fall: Uncovering the Hidden Risk Factors
Bibliographic record
Abstract
Background: India is expected to see an increase in the proportion of older adults from 8% in 2015 to 19% in 2050 due to the demographic transition. Identification of frailty in the elderly has become important because of its predictive nature of postoperative complications, hospitalization, and death. Frail older adults are likely to experience falls, which is a major public health problem. In this study, we have tried to describe the factors associated with frailty and falls. Materials and Methods: A cross-sectional survey was done of people 60 years and older attending the tertiary care geriatric outpatient department and inpatient department. Screening for frailty was done using Fried’s and Tilburg frailty scores and any one positive was considered frail. Frail individuals who reported having a fall in the past 1 year were studied for multiple factors. Results: On screening 332 older adults, a quarter (79, 23.8%) reported falls in the past 1 year. Females reported more falls with a male-to-female ratio of 1–1.63. Significant association of frail elderly with falls was found with depression ( P = 0.046), abuse ( P = 0.035), reduced gait speed ( P = 0.025), and difficulty in balancing ( P = 0.001). Conclusion: The study suggests screening of frail elderly presenting with falls for depression, abuse, reduced gait speed, and poor balance. Further research is needed to confirm these findings and to explore their role in preventive and curative strategies.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".