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Record W2166689119 · doi:10.1177/0898264311422598

The Role of Medications in Predicting Activity Restriction Due to a Fear of Falling

2011· article· en· W2166689119 on OpenAlexafffund
Dawn M. Guthrie, Paula C. Fletcher, Katherine Berg, Evelyn Williams, Nicole Boumans, John P. Hirdes

Bibliographic record

VenueJournal of Aging and Health · 2011
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsSunnybrook Health Science CentreUniversity of WaterlooHomewood Research InstituteUniversity of TorontoHealth Sciences CentreWilfrid Laurier University
FundersHealth CanadaU.S. Department of Veterans Affairs
KeywordsFear of fallingFalling (accident)Berg Balance ScaleActivities of daily livingMedicineBalance (ability)Multivariate analysisStair climbingGerontologyPhysical medicine and rehabilitationPhysical therapyPsychologyInjury preventionPoison controlPsychiatryInternal medicineMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the role of medication use and other factors in predicting activity restriction due to a fear of falling (AR/FF). METHODS: Older adults were assessed twice with the interRAI Community Health Assessment and the Berg Balance Scale (BBS). The main outcome was limiting going outdoors due to an AR/FF. Medications were recorded by trained assessors. RESULTS: Participants (n = 441) had a mean age of 80.3 (SD = 7.1) years, most were aged 65+ (96.8%) and 29.3% reported activity restriction. Taking nervous system active or cardiovascular medications was associated with AR/FF. In a multivariate model, the main predictors were having 3+ comorbid health conditions, lower (i.e., worse) scores on the BBS, having difficulty with climbing stairs, and having a visual impairment. DISCUSSION: Modifiable risk factors, related to functional impairments, such as difficulties with balance and vision, appear to be more important predictors than medications.

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)0.000
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 score0.096
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.052
GPT teacher head0.389
Teacher spread0.337 · 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

Citations11
Published2011
Admission routes2
Has abstractyes

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