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Record W2752198034 · doi:10.13189/saj.2017.050302

Relationships between the Functional Movement Screen Score and Y-Balance Test Reach Distances

2017· article· en· W2752198034 on OpenAlexaff
Leila Kelleher, Ryan J. Frayne, Tyson A.C. Beach, Jordin M. Higgs, Andrew M. Johnson, James P. Dickey

Bibliographic record

VenueInternational journal of human movement and sports sciences · 2017
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of TorontoDalhousie UniversityHumber PolytechnicWestern University
Fundersnot available
KeywordsMovement (music)Test (biology)Balance (ability)Functional movementPsychologyPhysical medicine and rehabilitationMedicineBiologyArtEcology

Abstract

fetched live from OpenAlex

Background: The Functional Movement Screen (FMS) is used to evaluate key movement patterns, functional symmetry, and identify individuals that are at elevated risk of injury. The purpose of this study was to assess whether dynamic postural control is a significant component of the composite FMS score by comparing it with Y-Balance Test (YBT) reach distances. Methods: Seventy-eight participants (including 40 males) performed the standardized FMS protocol followed by the YBT. The YBT reach distances were normalized to leg length and averaged between sides and trials. The individual reach directions were evaluated, and were also summed to form an aggregate YBT distance (TotalY). Results: We observed weak correlations between the composite FMS score and normalized posterolateral reach, normalized posteromedial reach, and the TotalY (r=0.36, 0.37, and 0.36, respectively; all p< 0.05). There was no correlation between the composite FMS score and normalized anterior reach (r=0.22; p=0.053). Together these findings demonstrate partial correspondence between the two tests. Conclusion: This indicates that dynamic postural control is a small component of the aggregate FMS score.

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)0.000
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 score0.016
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.059
GPT teacher head0.332
Teacher spread0.273 · 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
Published2017
Admission routes1
Has abstractyes

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