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Record W2727823387 · doi:10.1093/geroni/igx004.3319

PATHWAYS LINKING FEAR OF FALLING (FOF) AND INCIDENCE OF FUNCTIONAL LIMITATION IN OLDER ADULTS

2017· article· en· W2727823387 on OpenAlexaff
Beatriz Alvarado, Afshin Vafaei, Mohammad Auais, Catherine M. Pirkle, Carmen‐Lucía Curcio

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsQueen's University
Fundersnot available
KeywordsFear of fallingStairsPhysical medicine and rehabilitationGaitMediationDepression (economics)Incidence (geometry)PsychologyPoison controlPhysical therapyMedicineInjury preventionEngineeringMedical emergency

Abstract

fetched live from OpenAlex

We used longitudinal data from the International Mobility in Aging Study (n=1,355) to explore pathways between FOF measured by the Falls Efficacy Scale-International and incidence of functional limitation in old age. Outcomes were incidence cases of: 1) self-reported difficulty climbing a flight of stairs or walking 400 metres (mobility limitation), and 2) scoring <9 in the Short Physical Performance Battery [SPPB] (physical performance limitation). The potential pathways (gait speed, physical activity, balance, depression, and grip strength) were selected based on available theories and examined using mediation analysis adjusting for age, sex, site, cognition, and comorbidities. Total and direct effects of FOF on both outcomes were significant in all models. After adjustment, the relationship between FOF and mobility limitation was mediated only through gait speed. In women, no mediator linked FOF to SPPB; however, in men depression was a significant mediator. FOF has a strong direct effect on disability in older adults. Depression and gait speed partially account for incidence of disability.

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)0.003
metaresearch head score (Gemma)0.010
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.057
GPT teacher head0.357
Teacher spread0.300 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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