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Record W4362698606 · doi:10.3390/mps6020041

Intrarater Reliability and Analysis of Learning Effects in the Y Balance Test

2023· article· en· W4362698606 on OpenAlexaff
Olli Kattilakoski, Noora Kauranen, Mari Leppänen, Pekka Kannus, Kati Pasanen, Tommi Vasankari, Jari Parkkari

Bibliographic record

VenueMethods and Protocols · 2023
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsAlberta Children's HospitalAlberta Bone and Joint Health InstituteUniversity of Calgary
Fundersnot available
KeywordsIntra-rater reliabilityTest (biology)Reliability (semiconductor)Reliability engineeringBalance (ability)PsychologyBalance testComputer scienceEngineeringDevelopmental psychologyRating scaleGeologyPhysicsNeuroscience

Abstract

fetched live from OpenAlex

While the general reliability of the Y balance test has been previously found to be excellent, earlier reviews highlighted a need for a more consistent methodology between studies. The purpose of this test-retest intrarater reliability study was to assess the intrarater reliability of the YBT using different methodologies regarding normalisation for leg length, number of repetitions, and score calculation. Sixteen healthy adult novice recreational runners aged 18-55 years, both women and men, were reviewed in a laboratory environment. Mean calculated scores, intraclass correlation coefficient, standard error of measurement, and minimal detectable change were calculated and analysed between different leg length normalisation and score calculation methods. The number of repetitions needed to reach a plateauing of results was analysed from the mean proportion of maximal reach per successful repetition. The intrarater reliability of the YBT was found to be good to excellent, and it was not affected by the method of score calculation or leg length measurement. The test results plateaued after the sixth successful repetition. Based on this study, it is suggested to use anterior superior iliac spine-medial malleolus length for leg length normalisation because this method was proposed in the original YBT protocol. At least seven successful repetitions should be performed to reach a result plateau. The average of the best three repetitions should be used to mitigate possible outliers and account for the learning effects seen in this study.

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)0.002
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 score0.457
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.362
Teacher spread0.334 · 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

Citations21
Published2023
Admission routes1
Has abstractyes

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