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Record W7015064241

Relationship between core stability and Functional Movement Screening test in athletes

2019· other· en· W7015064241 on OpenAlexaboutno aff

Bibliographic record

VenueWielkopolska Digital Library · 2019
Typeother
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodTSG101DysgeusiaDiafiltrationLiquationTriacetinEmperipolesisDemotion
DOInot available

Abstract

fetched live from OpenAlex

Introduction and Aim. Functional Movement Screening (FMS™) tests provide beneficial information regarding the movement and stability in the kinetic chain. The core region of the body, as the basis of movement chain, accounts for the facilitation of force and torque transmission. The aim of the present study was to investigate the relationship between functional movement screen composite scores and core stability muscles endurance in athletes. Material and Methods. Forty-five male athletes with FMS scores ≤14 (LoFMS) and forty-five male athletes with FMS scores >14 (HiFMS) were studied. Stability of core muscles of the participants was investigated and compared using the McGill’s test. Results. The results of this study showed a significant difference in the mean stability of the anterior trunk muscles (p = 0.001), right side trunk muscles (p = 0.005) and left side trunk muscles (p = 0.001) between the athletes with LoFMS and HiFMS scores. Muscles’ endurance in the group with HiFMS score was significantly higher than the group LoFMS score (p = 0.001). However, there was no significant difference in the mean stability of the posterior trunk muscles between the two groups. In general, a significant difference was found between sum of core stability scores obtained from lumbar-pelvic stabilizer muscles in the posterior, anterior and lateral sides of athletes LoFMS and HiFMS scores. Conclusions. The results of this study showed that weakness in core stability can have a negative influence on movement patterns

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.048
GPT teacher head0.237
Teacher spread0.189 · 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
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

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