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Knowledge of Risk Factors for Falling Reported by Patients with Parkinson Disease

2007· article· en· W1978270796 on OpenAlexaff
Cheryl A Sadowski, Allyson C. Jones, Beverly Gordon, David Feeny

Bibliographic record

VenueJournal of Neuroscience Nursing · 2007
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFalling (accident)MedicineFear of fallingRisk factorParkinson's diseaseDiseaseRisk perceptionCross-sectional studyInjury preventionPoison controlPerceptionPsychologyPsychiatryMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

This study examined awareness of the risk factors for falling among a group of community dwelling patients with Parkinson disease (PD) using the Falls Risk Awareness Questionnaire (FRAQ). A cross-sectional survey of 28 patients who attended a Movement Disorders Clinic for treatment of PD was used. FRAQ is a 28-item self-administered survey that assesses the knowledge and perception of risk factors for falling. Demographic, medical, and medication data were gathered from both the participants and clinic charts. Twenty-three (82%) of the participants reported falls in the past; seven (30%) had fallen within the past month. Nineteen (68%) of the participants felt they were at risk for further falls. When asked to list potential risk factors for falling, only 14% could identify medication as a risk factor. Persons with PD are at substantial risk of falling, yet many appear to be unaware of common risk factors, especially medication use.

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)0.005
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.046
GPT teacher head0.387
Teacher spread0.341 · 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

Citations22
Published2007
Admission routes1
Has abstractyes

Explore more

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