IJCM_100A: Mountain Heights and Aging Insights: A Comprehensive Geriatric Assessment and Its Correlates in a Mesmerizing Hilly City of Northern India
Bibliographic record
Abstract
Background: Global population is experiencing a rapid increase in the proportion of older adults, posing unique challenges to healthcare systems. Objective: To conduct a comprehensive geriatric assessment in Shimla, a hilly city in Northern India Methodology: A total of 408 participants aged 60 years and above were included in the study. Data were collected through structured interviews using a specifically designed questionnaire covering wide range of topics including socio-demographic characteristics, medical history, functional assessment, cognitive function assessment, psychosocial assessment, nutritional assessment, physical examination, fall risk assessment, and polypharmacy assessment. Results: The study involved 408 participants, with an average age of 70 years, comprising 53% males and 47% females. Functional assessment indicated theneed for assistance in various activities of daily living (ADLs) (16-32%) and instrumental activities of daily living (IADLs) (28-42%) Cognitive function assessment using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) revealed cognitive impairment in 32% and 42%, respectively. The Clock Drawing Test indicated difficulties in time representation and spatial organization in 28%. Psychosocial assessment highlighted the prevalence of symptoms of depression in 36% and f anxiety in 27%. Nutritional assessment revealed that 29% were malnourished, and 34% were at high risk of malnutrition according to the Malnutrition Universal Screening Tool (MUST). Musculoskeletal disorders were found in 42%, while sensory impairments were observed in 28%. The fall risk assessment using the Timed Up and Go (TUG) test and the Berg Balance Scale indicated an increased risk of falls in 36% and 41%, respectively. Polypharmacy assessment highlighted that 82 were taking multiple medications concurrently. Conclusion: The findings emphasize the importance of addressing the specific challenges faced by older adults in such geographical contexts. The results can guide the development of effective healthcare interventions and policies that are tailored to the unique needs of older adults in hilly areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".