MétaCan
Menu
Back to cohort
Record W3205001626 · doi:10.4103/ijcm.ijcm_616_20

Frailty, disability, and mortality in a rural community-dwelling elderly cohort from Northern India

2021· article· en· W3205001626 on OpenAlexaboutno aff
Rakesh Kumar, Rama Shankar Rath, Ritvik Amarchand, GiridaraP Gopal, Debjani Ram Purakayastha, Reshmi Chhokar, VenkateshV Narayan, Aparajit Ballav Dey, Anand Krishnan

Bibliographic record

VenueIndian Journal of Community Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHazard ratioGerontologyCohortConfidence intervalOdds ratioCohort studyDemographyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: With increasing proportion of the elderly in the world, detecting and preventing frailty assumes importance to improve the quality of life and health. The study aimed to estimate the prevalence of frailty, disability and its determinants and their relation with mortality among community dwelling elderly cohort. MATERIALS AND METHODS: The study was conducted in a cohort in rural Haryana, India, and was followed till October 2018. Frailty was assessed using the Edmonton Frailty Scale and disability was assessed using the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) scale by trained physicians. RESULTS: The prevalence of frailty was found to be 47.3% (95% confidence interval [CI]: 44.0-50.8). The median WHODAS-2 score was found to be 10.4 (2.1-29.2). Those who were older (odds ratio [OR] - 2.5; 95% CI: 1.8-3.4), women (OR - 3.3; 95% CI: 2.2-4.9) and those with chronic disease (OR 2.3; 95% CI: 1.7-3.1) had higher rates of frailty. The adjusted hazard ratio of death among frail people was 4.7 (2.3-9.7). CONCLUSION: In this study we found the frailty is associated with the mortality among community dwelling elderly. Thus early identification of the frailty and its determinants may help us to reduce the mortality related to this.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.006
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 head0.047
GPT teacher head0.331
Teacher spread0.284 · 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.

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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIndian Journal of Community MedicineSame topicFrailty in Older AdultsFrench-language works237,207