A cross sectional observation study on quality of life and cognition in elderly population and their correlation at a tertiary care centre
Bibliographic record
Abstract
Background: India, the second most populous country is facing demographic transition. cognitive decline is one of the normative changes of aging; however, this may impact both physical and mental health of an individual. Quality of life is one of the measures of successful aging. This study was conducted to correlate the level of cognition and quality of life in elderly population. Our aim was to assess quality of life in geriatric population using OPQOL-35 and to assess cognitive assessment by MOCA and determine correlation of cognitive level with quality of life (QoL).Methods: A cross-sectional observational study was conducted among 110 elderly adults (above the age of 60 years. Montreal cognitive assessment (MoCA) was administered to assess the cognitive level. QoL was assessed by OPQOL-35.Results: Significant positive correlation was noted between quality of life and level of cognition scoring (with correlation coefficient 0.234).Conclusions: The study concluded that the level of cognition and quality of life of elderly adults are in positive correlation with each other. community level screening of elderly for cognitive dysfunction can be made even in resource poor settings. Early identification and referral of elderly with cognitive dysfunction will ensure successful aging.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".