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To Evaluate The Effectiveness Of Non-Pharmacological Multifactorial Interventions On Cognitive Impairment In Geriatric Patients

2025· article· en· W4411616149 on OpenAlexaboutno aff
Jiajia Cui, Junjun Zhu, Yanbin Wei, Quanjun Piao

Bibliographic record

VenuePakistan Journal of Pharmaceutical Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionMedicineDepression (economics)GerontologyQuality of life (healthcare)Psychological interventionProspective cohort studyMontreal Cognitive AssessmentPhysical therapyCohortPhysical medicine and rehabilitationCognitive impairmentPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.072
GPT teacher head0.520
Teacher spread0.448 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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