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Record W7132087673

Investigation of the Relationship Between Trunk Muscle Endurance and Static/Dynamic Balance in Healthy Adults

2021· article· tr· W7132087673 on OpenAlexaboutno aff
derya tüzün kaya, Özden Özkal

Bibliographic record

Venueİzmir Katip Celebi University · 2021
Typearticle
Languagetr
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsTrunkBalance testBalance (ability)
DOInot available

Abstract

fetched live from OpenAlex

Amaç: Bu çalışmanın amacı sağlıklı erişkinlerde gövde kas enduransı ile statik/dinamik denge arasındaki ilişkiyi incelemekti. Gereç ve Yöntem: Bu çalışmaya 51 birey (kadın=29; erkek=22) dahil edildi. Katılımcıların demografik bilgileri kaydedildi. Bireylerin gövde fleksör, ekstansör ve lateral kas endurans testleri McGill gövde kas endurans testi rehberine uygun olarak yapıldı. Bireylerin statik dengesi tek ayak üzerinde durma testi ile, dinamik dengesi ise Y denge testi ile değerlendirildi. Bulgular: Hiyerarşik regresyon analiz sonuçlarına göre, daha fazla gövde fleksör kas enduransının anterior (model 2, p<0,001, R2=0,581) ve posteromedial (model 2, p=0,004, R2=0,468) yönlerde daha yüksek Y denge test performansı ile ilişkili olduğu bulundu. Daha fazla gövde fleksör kas enduransı, posterolateral yönde daha yüksek Y denge test performansı ile ilişkili iken, ileri yaşın posterolateral yönde daha düşük Y denge test performansı ile ilişkili olduğu belirlendi (model 2, sırasıyla p<0,001 ve p=0,006, R2=0,436). Daha fazla gövde ekstansör kas enduransının daha yüksek statik denge test performansı ile ilişkili olduğu saptandı (model 2, p=0,001, R2=0,318). Sonuç: Sağlıklı erişkinlerde daha yüksek gövde fleksör ve ekstansör kas endurans sürelerinin daha iyi dinamik ve statik denge test performansı ile ilişkili olduğu gösterildi. Gövde kas enduransına ek olarak, ileri yaşın ise posterolateral yönde daha düşük dinamik denge test performansı ile ilişkili olduğu bulundu.

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 score0.005
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.033
GPT teacher head0.291
Teacher spread0.257 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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