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Record W4396770676 · doi:10.4103/pmrr.pmrr_16_24

Prevalence and Determinants of Frailty amongst the Elderly: A Study from a Tertiary Care Hospital in North India

2024· article· en· W4396770676 on OpenAlexaboutno aff
Nidhi Prakash Vadanere, Aninda Debnath, Anita Verma, Priyansha Gupta

Bibliographic record

VenuePreventive Medicine Research & Reviews · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGerontology

Abstract

fetched live from OpenAlex

Abstract Background: The elderly population in India is rising rapidly, which merits the need to develop an understanding of the prevalence and determinants of frailty in this age group. Materials and Methods: This study was conducted at a tertiary care hospital in New Delhi, India. The participants were over 60 years of age. The authors used the Edmonton Frailty Scale to assess frailty and a semi-structured questionnaire to assess sociodemographic variables, morbidity and lifestyle factors. Results: The mean age of participants in the study was 66.7 years (standard deviation ± 5.52). The prevalence of any type of frailty was 34.7% (95% confidence interval [CI] 29.9%–39.6%). Amongst the frail, 11.9% were vulnerable to frailty, 11.7% were mildly frail, 8.1% were moderately frail and 3% were severely frail. Significant associations were found with age, chronic diseases and smokeless tobacco use. Conclusion: There is a substantial prevalence of frailty amongst the elderly, with critical links to age, chronic disease and lifestyle choices. These findings underscore the necessity of integrating frailty assessment into routine geriatric care and primary healthcare services.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.229
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
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 science0.0000.000
Research integrity0.0000.001
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 head0.064
GPT teacher head0.416
Teacher spread0.352 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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