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Record W3107767543

하부승모근 강화 운동이 엘리트 양궁선수들의 통증, 기능 및 경기력 향상에 미치는 효과

2020· article· ko· W3107767543 on OpenAlexaboutno aff
김은국, 김진호, 유진영

Bibliographic record

VenueThe Korean Journal of Sports Medicine · 2020
Typearticle
Languageko
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsScapulaShouldersMedicinePhysical therapyPhysical medicine and rehabilitationAcromionElectromyographyShoulder jointRotator cuffSurgery
DOInot available

Abstract

fetched live from OpenAlex

Purpose: The primary aim of this study was to examine the effects of 8-week lower trapezius strengthening exercise (LTSE) on shoulder pain, function and archery performance. The secondary aim was to identify main factors that have something to do with injury prevention and performance enhancement for elite archers. Methods: Thirty-one elite archers were recruited and evenly assigned into the LTSE group (n=16) and into the control group (n=15) based on gender and athletes’ career. Shoulder pain was evaluated using Numeric pain rating scale (NPRS). Shoulder function was assessed using the Western Ontario Shoulder Instability Index (WOSI), upper quarter Y balance test (UQYBT), Trapezius and Deltoid muscle activity ratios by surface electromyography and the angle of scapula elevation/abduction by 3-dimentional motion analyses. Archery performance was estimated using draw force line (DFL) angle at full bowstring draw position and the scores acquired from real archery shooting. After the baseline measurements, the 8-week LTSEs were implemented and the post-exercise measurements were conducted. Results: In the LTSE group, NPRS score and WOSI score significantly decreased after exercise program. The activity ratio of upper to lower trapezius muscle, scapula elevation angle and the DFL angle were also significantly reduced. The UQYBT scores significantly increased on both shoulders. Conclusion: Eight weeks of LTSE has reduced shoulder pain in archers and improved shoulder function and performance factors.

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)0.006
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0130.000

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 head0.023
GPT teacher head0.280
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.

Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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