HUBUNGAN GLENOHUMERAL INSTABILITY TERHADAP TERJADINYA SWIMMER’S SHOULDER PADA KLUB RENANG DI KABUPATEN BADUNG
Bibliographic record
Abstract
Swimmer’s Shoulder menggambarkan kondisi nyeri bahu yang dialami oleh perenang, salah satu faktor resikonya yaitu glenohumeral instability yang merupakan kondisi ketidakstabilan bahu. Kondisi bahu yang tidak stabil dapat menurunkan performa atlet yang dapat meningkatkan resiko terjadinya swimmer’s shoulder. Penelitian ini bertujuan untuk mengetahui hubungan glenohumeral instability terhadap terjadinya swimmer’s shoulder pada klub renang di Kabupaten Badung. Penelitian ini menggunakan rancangan observasional analytic dengan pendekatan metode cross sectional study dan teknik nonprobability sampling dengan jenis purposive sampling dalam. Sampel berjumlah 67 orang, yang diukur glenohumeral instability nya menggunakan tes spesifik yaitu apprehension test dan sulcus sign test dengan skala Visual Analog Scale (VAS) dan swimmer’s shoulder menggunakan kuesioner Western Ontario Rotator Cuff (WORC). Analisis data yang digunakan yaitu teknik spearman’s rho dengan nilai p=0,001, sampel dengan kondisi bahu yang stabil berjumlah 50 orang (74,6%) dan dominan berpotensi rendah terjadinya swimmer’s shoulder (74,6%) sedangkan kondisi bahu yang tidak stabil 17 orang (25,4%) dan dominan berpotensi sedang terjadinya swimmer’s shoulder (25,4%). Hasil analisis data ini menunjukkan bahwa terdapat hubungan yang signifikan antara glenohumeral instability dengan swimmer’s shoulder pada klub renang di Kabupaten Badung. Kata Kunci: perenang, swimmer’s shoulder, glenohumeral instability
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".