Frailty, disability, and mortality in a rural community-dwelling elderly cohort from Northern India
Bibliographic record
Abstract
INTRODUCTION: With increasing proportion of the elderly in the world, detecting and preventing frailty assumes importance to improve the quality of life and health. The study aimed to estimate the prevalence of frailty, disability and its determinants and their relation with mortality among community dwelling elderly cohort. MATERIALS AND METHODS: The study was conducted in a cohort in rural Haryana, India, and was followed till October 2018. Frailty was assessed using the Edmonton Frailty Scale and disability was assessed using the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) scale by trained physicians. RESULTS: The prevalence of frailty was found to be 47.3% (95% confidence interval [CI]: 44.0-50.8). The median WHODAS-2 score was found to be 10.4 (2.1-29.2). Those who were older (odds ratio [OR] - 2.5; 95% CI: 1.8-3.4), women (OR - 3.3; 95% CI: 2.2-4.9) and those with chronic disease (OR 2.3; 95% CI: 1.7-3.1) had higher rates of frailty. The adjusted hazard ratio of death among frail people was 4.7 (2.3-9.7). CONCLUSION: In this study we found the frailty is associated with the mortality among community dwelling elderly. Thus early identification of the frailty and its determinants may help us to reduce the mortality related to this.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".