Frailty among Elderly visiting the Outpatient Department of Internal Medicine in a Tertiary Care Centre
Bibliographic record
Abstract
Introduction: Frailty is a common geriatric condition and is highly widespread among elderly people. There are global challenges to meeting the healthcare needs of the elderly with a rapidly growing elderly population. The aim of this study was to assess the prevalence of frailty among the elderly visiting the Outpatient Department of Internal Medicine in a tertiary care centre. Methods: A cross-sectional study was done in the Internal Medicine Outpatient Department in a tertiary care centre from 01 March 2024 to 30 April 2024 after obtaining ethical approval from the Institutional Review Board. Convenience sampling was done. Data were collected using Edmonton Frail Scale Acute Care Version by face-to-face interview technique. Data were entered and analyzed in IBM SPSS Statistics version 20.0. Results: Among 168 elderly patients, 75 (44.64%) [37.12–52.16, 95% Confidence Interval] were found to be frail whereas 93 (55.36%) were not frail (non-frail+vulnerable). After multivariate binary logistic regression analysis, the independent factors of frailty are age (AOR 3.942; CI 1.737-8.950), number of medicines (AOR 9.721; CI 3.408-27.729) and physical activity (AOR 0.233; CI 0.81-0.674). Conclusions: The prevalence of frailty among elderly people in this study was comparable to similar studies in different settings. The associated factors of frailty were found to be age, physical activity and number of medicines taken in our study. These factors could be taken into consideration while doing health assessment of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".