Prediction of fall-risk factors specific to the old-age Indian population
Bibliographic record
Abstract
Abstract Background/Objectives The health care infrastructure of India, designed to treat acute problems, can benefit from preventive medicine-based policies that address chronic and non-communicable issues of relevance to India’s growing elderly population. Unintentional fall related injuries are one such issue whose economic burden can be streamlined with proper preventative public health policies. It is imperative that fall-risk factors specific to the Indian population be identified and analyzed for use in geriatric falls-risk assessment. We aim to determine factors predictive of falls in the aging Indian population in this study using Wave 1 data from the World Health Organization Study on Global Ageing and Adult Health (WHO SAGE) in India. Methods Cross-sectional analysis of results from WHO SAGE Wave 1 was conducted. Multivariate analysis was used to determine risk factors of falls specific to the Indian population in adults over the age of 50. Prediction models were created and evaluated using these risk factors and their performances were evaluated. SAGE Wave 1 India was implemented in six states that together provided nationally representative samples. Multistage stratified sampling was used to select these states and systematic sampling was used to select households from villages and urban districts within these states. Data from all individuals over the age of 50 in selected households was compiled for analysis. Findings 34 fall risk factors specific to the Indian population were determined. The model that did not weigh the factors was determined as the best model for possible use in clinically assessing old-age adults at risk for falling. Furthermore, 6 risk factors in the Indian census were used to identify falls risks hotspots on a district-level map of India. Conclusion This analysis can be used in public health policy recommendations and can form a basis for assessing and addressing falls-risk issues in India.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".