EFEKTIVITAS KOMBINASI ELECTROTHERAPY DAN CORE STABILITY EXERCISE PADA PASIEN LOW BACK PAIN MYOGENIC
Bibliographic record
Abstract
Low back pain myogenic adalah salah satu gangguan muskuloskeletal yang diakibatkan karena spasme otot dan adanya ketidakseimbangan kinerja otot inti dan lumbopelvic sehingga terjadi nyeri dan keterbatasan aktifitas sehari-hari.Penatalaksanaan fisioterapi yang digunakan yaitu penggabungan electrotherapy dan core stability exercise (CSE) pada pasien low back pain myogenic.Adapun tujuan penelitian ini untuk mengetahui pengaruh kombinasi electrotherapy dan core stability exercise dalam mengurangi nyeri dan meningkatkan kapasitas fungsional pasien dengan low back pain myogenic. Desain penelitian ini adalah eksperimental pre-posttest design dengan responden 20 orang yang memenuhi kriteria inklusi dan eksklusi menggunakan teknik accidental sampling yang di laksanakan pada bulan Juni - Agustus 2023. Responden dilakukan pengukuran skala nyeri menggunakan NPRS (Numeric Pain Rating Scale) dan pengukuran aktifitas fungsional dengan Quebec Back Pain Disability Scale sebelum dan sesudah dilakukan treatment fisioterapi,yakni; pemberian Microwave Dhiatermi, Transcutaneus Electrical Nerve Stimulation,dan Core Stability Exercise (CSE) setiap 2x seminggu selama 4 minggu. Menggunakan uji hipotesis Wilcoxon dengan hasil nilai median NPRS; (1) pre sebanyak 6,00 ; (2) post sebanyak 2,00 dan (3) nilai P sebanyak 0,00, sedangkan nilai Quebec; (1) pre sebanyak 25,50 ; (2) post sebanyak 7,50 dan (3) nilai P sebanyak 0,00. Maka dapat disimpulkan pemberian kombinasi electrotherapy dan Core Stability Exercise (CSE) efektif dalam menurunkan nyeri dan memperbaiki kapasitas fungsional pada kasus low back pain myogenic
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".