To Evaluate The Effectiveness Of Non-Pharmacological Multifactorial Interventions On Cognitive Impairment In Geriatric Patients
Bibliographic record
Abstract
This prospective, cohort study was planned to assess the efficacy of nonpharmacological multifactorial approach for treating cognitive decline in patients of geriatric age group. This research involved 120 patients and caregivers, ≥ 65 years, with cognitive impairment, was a consecutive sample from a tertiary hospital. Measures involved learning and problem solving tasks, motor movement, company, and food choices that addressed participants' requirements. Cognitive status was evaluated by MMSE and MoCA, physical performance measured with TUG, depression with GDS and nutritional status with BMI and blood sample. Subsequent evaluations were done at 3 month interval for 1 year. At the end of 12 months, improvement in cognitive function was noted by a mean of 4.0 points in MMSE, and mean of 3.8 points in MoCA. Physical fitness, depression status, nutritional status, social integration and sleep quality were also found to have significant changes across time. Also, the studies revealed an enhancement of patients' satisfaction as well as a reduction in caregiver burden. These results raise the possibility that approaches targeting multiple risk factors of cognitive decline can effectively promote cognitive function and general quality of life in elderly people.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".