Efektivitas terapi latihan peregangan dalam manajemen osteoartritis lutut
Bibliographic record
Abstract
Pendahuluan: Terapi latihan peregangan merupakan terapi utama dan mudah dilakukan untuk osteoartritis lutut, penyebab utama disabilitas dan nyeri di dunia. Saat ini, standar pemberian terapi dan efeknya pada osteoartritis lutut masih bervariasi. Tinjauan pustaka ini bertujuan merangkum efektivitas terapi latihan peregangan terhadap penanganan osteoartritis lutut. Metode: Artikel ini menggunakan tinjauan pustaka dari berbagai literatur artikel penelitian mengenai efektivitas latihan peregangan terhadap osteoartritis lutut. Hasil: Latihan peregangan dapat dilakukan sebagai latihan tunggal ataupun bersamaan dengan latihan lainnya. Latihan ini memberikan efek penurunan nyeri, peningkatan lingkup gerak sendi, dan penurunan disabilitas. Efek-efek tersebut dinilai dengan instrumen Visual Analog Scale (VAS), pemeriksaan lingkup gerak sendi dengan goniometer, Knee Injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster University Osteoarthritis Index (WOMAC), dan Lequesne’s Index. Peregangan dapat dilakukan secara bervariasi dengan target otot utama adalah kuadrisep dan hamstring selama 15-30 detik, empat sampai sepuluh kali repetisi, dengan frekuensi setiap hari hingga tiga kali seminggu dalam bentuk teknik peregangan statis atau proprioceptive neuromuscular facilitation (PNF). Hasil yang signifikan sudah dapat terlihat dalam enam minggu hingga tiga bulan. Simpulan: Latihan peregangan menunjukkan efek yang baik dalam menurunkan nyeri, meningkatkan lingkup gerak sendi, menurunkan disabilitas, dan meningkatkan kualitas hidup, terutama latihan menggunakan metode peregangan PNF.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".