HUBUNGAN ANTARA INDEKS MASA TUBUH DENGAN TINGGI SKOR WESTERN ONTARIO AND MCMASTER UNIVERSITY OSTEOARTHRITIS INDEX PADA PASIEN OSTEOARTHRITIS LUTUT DI RSPAL DR. RAMELAN SURABAYA
Bibliographic record
Abstract
IMT (Indeks Masa Tubuh) adalah suatu indeks statistik yang menggunakan berat dan tinggi badan seseorang untuk memberikan estimasi pengukuran lemak tubuh seseorang. Nilai IMT ≥ 30 menunjukkan seseorang tersebut mengalami obesitas. Obesitas merupakan salah satu faktor risiko osteoarthritis lutut, dimana untuk menilai tingkat keparahan osteoarthritis lutut salah satunya menggunakan penilaian tinggi skor WOMAC (Western Ontario and McMaster University Osteoarthritis Index). WOMAC adalah pengukuran yang digunakan untuk menilai pasien dengan osteoarthritis pada ekstremitas bawah. Penelitian ini dilakukan untuk mengetahui hubungan antara indeks masa tubuh dengan tinggi skor WOMAC pada pasien osteoarthritis lutut di RSPAL Dr. Ramelan Surabaya. Penelitian ini termasuk kedalam jenis penelitian analitik observasional dengan desain penelitian menggunakan cross sectional study. Data yang diperoleh dari pengisian kuesioner WOMAC dilakukan pada 29 pasien osteoarthritis lutut di RSPAL Dr. Ramelan Surabaya. Pemilihan sampel menggunakan total sampling. Berdasarkan hasil uji korelasi Spearman didapatkan hasil signifikansi antara indeks masa tubuh dengan tinggi skor WOMAC sebesar (p=0,182). Artinya variabel indeks masa tubuh tidak berhubungan dengan tinggi skor WOMAC. Tidak ada hubungan antara indeks masa tubuh dengan tinggi skor WOMAC pada pasien osteoarthritis lutut di RSPAL Dr. Ramelan Surabaya.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.032 | 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".