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Record W2981301885 · doi:10.1017/cjn.2019.309

Vestibular Exercises as a Fall Prevention Strategy in Patients with Cognitive Impairment

2019· article· en· W2981301885 on OpenAlexaffvenue
Brenda Varriano, Shaleen Sulway, Curtis Wetmore, Wanda Dillon, Karen Misquitta, Namita Multani, Cassandra Jessica Anor, María Araceli García Martínez, Elena Cacchione, John Rutka, Maria Carmela Tartaglia

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2019
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsToronto Western HospitalToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsVestibular systemMedicineCognitive impairmentPhysical therapyRehabilitationVestibular rehabilitationFall preventionCognitionPhysical medicine and rehabilitationInjury preventionPoison controlAudiologyEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

Vestibular impairment (VI) and cognitive impairment (CI) are risk factors for senior falls. We tested the feasibility of a self-directed 12-week vestibular rehabilitation (VR) program in Memory Clinic patients (65 years+) with a fall, CI and VI. We assessed recruitment, exercise adherence and ability to complete questionnaires/assessments. Twelve patients with CI and falls were screened and 8/12 (75% - prevalence) had VI. All patients completed the screening tests/questionnaires (100% - completeness); 7/8 patients were recruited (87.5% - recruitment); 1/7 (85.7% - attrition) patient attended follow-up. VI is prevalent in patients with CI experiencing falls but traditional VR is not feasible, so a novel delivery of VR must be explored.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.021
GPT teacher head0.256
Teacher spread0.236 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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