Pemberian Terapi Latihan, Ultrasound (US) Serta Transcutaneous Electrical Nerve Stimulation (TENS) Pada Pasien Osteoarthritis Knee Bilateral
Bibliographic record
Abstract
Osteoarthritis (OA) merupakan penyakit degeneratif yang berkembang lambat dan tersebar luas prevalensinya meningkat seiring bertambahnya usia pasien. Tujuan dari studi ini untuk mengendalikan nyeri yang dirasakan pasien serta meningkatkan kemampuan fungsional pada kegiatan sehari-hari yang dilakukan pasien. Penelitian ini termasuk case study yang dilakukan di salah satu rumah sakit yang ada di kota Surakarta pada seorang pasien Ny. S, Berusia 63 tahun, berprofesi sebagai ibu rumah tangga. Pasien mengeluhkan nyeri pada saat gerakan dari jongkok ke berdiri terutama saat gerakan sujud ke berdiri saat beribadah yaitu salat Nyeri yang di rasakan pasien terkadang hilang saat kondisi istirahat dan tidak terlalu banyak aktifitas berat yang dilakukan. Pasien terapi selama 2 x/ minggu dalam 3 minggu, satu kali terapi mengikuti selama 30 menit. Pasien diberikan terapi berupa terapi latihan, ultrasound dan tens. nyeri diukur menggunakan Visual Descriptive Scale (VDS), dan Western Ontario dan McMaster Universities Arthritis Index (WOMAC) untuk mengukur aktivitas fungsional pasien. Modalitas fisioterapi seperti Pemberian Latihan Quadriceps Setting, Passive Stretching, Ultrasound (US), serta Transcutaneous Electrical Nerve Stimulation (TENS) yang diberikan sebanyak 4 kali pertemuan belum mampu meningkatkan aktivitas fungsional sehari-hari serta terdapat sedikit penurunan nyeri
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".