HUBUNGAN INDEKS MASSA TUBUH (IMT) DENGAN NYERI, KEKAKUAN SENDI DAN AKTIVITAS FISIK PADA PASIEN OSTEOARTRITIS LUTUT DI POLIKLINIK BEDAH ORTOPEDI RSU DR. SOEDARSO PONTIANAK TAHUN 2013
Bibliographic record
Abstract
Latar Belakang: Penelitian telah menunjukkan bahwa indeks massa tubuh (IMT) merupakan faktor risiko penting terjadinya osteoartritis lutut. Namun, sedikit diketahui apakah IMT juga berhubungan dengan keparahan gejala diantara individu dengan osteoartritis lutut. Tujuan: Tujuan dari penelitian ini adalah untuk mencari hubungan antara nyeri, kekakuan sendi dan aktivitas fisik pada pasien osteoartritis lutut. Metodologi: Penelitian ini merupakan penelitian analitik dengan pendekatan potong lintang. Sebanyak 43 pasien dengan OA lutut yang datang ke Klinik Bedah Ortopedi dilibatkan dalam penelitian ini. Diagnosis berdasarkan pada kriteria OA lutut dari American College of Rheumatology (ACR). Pengukuran berat dan tinggi badan dilakukan pada setiap responden untuk menghiting IMT. Responden juga menyelesaikan kuesioner Western Ontario and Mcmaster Universities Osteoarthritis Index (WOMAC) Hasil: Indeks massa tubuh memiliki hubungan yang bermakna dengan beratnya gejala OA lutut, untuk WOMAC nyeri (p=0,002), WOMAC kekakuan sendi (p=0,000), dan WOMAC aktivitas fisik (p=0,000). Di lain pihak, tidak terdapat hubungan yang signifikan antara semua subkategori WOMAC dengan usia. Kesimpulan: Berdasarkan hasil analisis ini, dapat disimpulkan bahwa pasien OA lutut dengan IMT yang tinggi memiliki risiko lebih besar untuk mengalami gejala yang lebih berat. Kata kunci: Indeks massa tubuh, Osteoartritis lutut, Indeks WOMAC
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".