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Correlation between cognition and risk of falls in elderly

2023· article· en· W4386998383 on OpenAlexaboutno aff
Saraswati Iyer, Elton John Menezes

Bibliographic record

VenueInternational Journal of Neurology Sciences · 2023
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionBalance (ability)Montreal Cognitive AssessmentFalling (accident)CorrelationCognitive declineGerontologyMedicineElderly peopleAge groupsPopulationPositive correlationPsychologyDemographyDementiaCognitive impairmentPhysical therapyInternal medicineDiseasePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Objective: This study was to done to assess the cognitive function with the risk of fallling across participants in the elderly age group, investigating associations between the 2 aspects. Method: 31 healthy elderly subjects in the age group of 60 to 80 were selected for the study. Their cognitive and balance profiles were assessed using the MOCA and POMA scales respectively. This data was used to identify the relationship between cognitive decline and fall risk in the elderly population. Results: Findings revealed significant differences in MOCA and POMA scores across the age groups. It was observed that there was a positive correlation of MoCA (cognition) and POMA (balance) with statistically significant, moderately positive correlation (r = 0.577, p < 0.001) which means greater the age, higher is the risk of falling Conclusion: Community-dwelling elderly individuals with cognitive impairments displayed an increased risk of falling.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.397
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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