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Record W2121912602 · doi:10.3138/ptc.2011-12bh

Risk Factors for Falls in Older Adults with Lower Extremity Arthritis: A Conceptual Framework of Current Knowledge and Future Directions

2012· article· en· W2121912602 on OpenAlexaffvenue
Cathy M. Arnold, Nancy C. Gyurcsik

Bibliographic record

VenuePhysiotherapy Canada · 2012
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicinePhysical therapyBalance (ability)Fall preventionOsteoarthritisPhysical medicine and rehabilitationPopulationHip fractureInjury preventionGerontologyDiseasePoison controlOsteoporosisAlternative medicineInternal medicineMedical emergencyEnvironmental healthPathology

Abstract

fetched live from OpenAlex

PURPOSE: As the numbers of Canadians aged 65 years and over increases over the next 20 years, the prevalence of chronic conditions, including arthritis, will rise as will the number of falls. Although known fall-risk factors are associated with hip and knee osteoarthritis (OA), minimal research has evaluated fall and fracture risk and/or rates in this population. Thus, the purpose was to summarize research on fall and fracture risk in older adults with hip or knee OA and to develop a conceptual framework of fall-risk screening and assessment. METHOD: The International Classification of Functioning, Disability and Health, clinical practice guidelines for fall-risk screening, and a selected literature review were used. RESULTS: Gaps exist in our knowledge of fall and fracture risk for this population. Muscle performance, balance, and mobility impairments have been identified, but little is known about whether personal and environmental contextual factors impact fall and fracture risk. Physical activity may help to prevent falls, but non-adherence is a problem. CONCLUSION: A need exists to assess fall risk in older adults with hip and knee OA. Promoting regular physical activity by focusing on disease- and activity-specific personal contextual factors may help direct treatment planning.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.452
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.327
Teacher spread0.315 · 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

Citations38
Published2012
Admission routes2
Has abstractyes

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