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Record W2106134880 · doi:10.1682/jrrd.2013.09.0206

Fukuda and Babinski-Weil tests: Within-subject variability and test-retest reliability in nondisabled adults

2014· article· en· W2106134880 on OpenAlexaff
Nicole Paquet, Anne Taillon‐Hobson, Yves Lajoie

Bibliographic record

VenueThe Journal of Rehabilitation Research and Development · 2014
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychologyTest (biology)Reliability (semiconductor)

Abstract

fetched live from OpenAlex

The Fukuda Stepping Test and the Babinski-Weil test are clinical assessments that presumably reveal unilateral vestibular hypofunction. However, abnormal performances on both of these two tests have been found in nondisabled individuals. This study compared measures of lateral and longitudinal displacements and body rotation for both tests, as well as the within-subject variability and test-retest reliability of the measures. In addition, correlations between hand and foot dominance and these measures were studied. Fifty young, nondisabled participants performed three trials of the 100-step Fukuda test and three trials of the Babinski-Weil test. The testing session was repeated 7 d later (retest). Lateral displacement, body rotation, and within-subject variability of these two measures were larger on the Fukuda than the Babinski-Weil test. No difference in test-retest reliability was found between the two tests, and a significant correlation was found between body rotation on the Fukuda test and score on the Waterloo Footedness Questionnaire. There may have been smaller variability in results of the Babinski-Weil test because it contains fewer steps (36) than the 100-step Fukuda test. Future research should compare tests with an equal number of steps; but, in the meantime, the Babinski-Weil test seems to have better psychometric properties than the Fukuda test, at least in nondisabled individuals.

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)0.012
metaresearch head score (Gemma)0.050
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.303
Teacher spread0.280 · 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.

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

Citations21
Published2014
Admission routes1
Has abstractyes

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