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Record W4388907341 · doi:10.3390/healthcare11233020

Concurrent Validity and Test–Retest Reliability of the 3-Meter Backward Walk Test in Stroke Survivors

2023· article· en· W4388907341 on OpenAlexaff
Ali Kapan, Miloš Ristić, Lin Yang, Gottfried Kranz, Thomas Waldhör

Bibliographic record

VenueHealthcare · 2023
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsTest (biology)Reliability (semiconductor)Concurrent validityPsychologyStroke (engine)MetrePhysical medicine and rehabilitationReliability engineeringMedicineClinical psychologyPsychometricsEngineeringInternal consistencyPhysics

Abstract

fetched live from OpenAlex

In the context of evaluating physical function in individuals with stroke, the 3-Meter Backward Walk Test (3MBWT) emerges as a potential tool of interest. The purpose of this study was to assess the test-retest reliability and concurrent validity of the 3MBWT and its correlation with falling incidents. Conducted in a neurological rehabilitation center, 35 ambulatory individuals with stroke were enrolled within a month post-stroke onset. These participants, with a Functional Ambulation Category score of ≥4, underwent the 3MBWT, Functional Gait Assessment (FGA), 10-Meter Walk Test (10MWT), and 6-Minute Walk Test (6MWT) under the supervision of different physiotherapists. The results indicate that the 3MBWT demonstrated high reliability, with an Intraclass Correlation Coefficient of 0.97 (95% CI: 0.95, 0.98). It also showed significant concurrent validity with other established walking tests like the 6MWT (r = -0.78) and 10MWT (r = 0.71), with a moderate correlation with the FGA (r = -0.54). No marked differences in test outcomes were observed between participants based on their fall history. Conclusively, the 3MBWT proves to be highly reliable and agrees well with existing walking function assessments for stroke patients, suggesting its potential as a time-efficient alternative.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.012
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.045
GPT teacher head0.331
Teacher spread0.286 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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