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A cross sectional observation study on quality of life and cognition in elderly population and their correlation at a tertiary care centre

2022· article· en· W4307592645 on OpenAlexaboutno aff
Mrinalini Motlag, K. S. Tony, Shweta V. Madavi, Amol G. Bhondre, M. S. Pandharipande, Deepti Deshmukh

Bibliographic record

VenueInternational Journal of Research in Medical Sciences · 2022
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCognitionMontreal Cognitive AssessmentQuality of life (healthcare)GerontologyPopulationCross-sectional studyNormativeObservational studyReferralCorrelationCognitive impairmentEnvironmental healthPsychiatryFamily medicinePathology

Abstract

fetched live from OpenAlex

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 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)0.008
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 score0.010
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.197
GPT teacher head0.533
Teacher spread0.336 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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