Uji Inter-Rater Reliability Western Ontario and Mcmaster University (WOMAC) Osteoarthritis Index pada Pasien Osteoarthritis Knee
Bibliographic record
Abstract
Penderita Osteoarthritis (OA) Knee tercatat sebanyak 5% dari total seluruh penderita OA knee di dunia sehingga diperlukan alat ukur yang relevan dan reliable untuk mengukur activities daily living (ADL) dan body function pada penderita OA. Selain menguji apakah alat ukur tersebut valid dan reliabel, diperlukan uji inter-rater reliability dari Indeks The Western Ontario and McMaster University Osteoarthritis Index (WOMAC) untuk digunakan di Indonesia. Penelitian ini adalah psychometric study dengan pendekatan cross-sectional untuk menguji inter-rater reliability pada instrumen pengukuran WOMAC yang diujikan kepada fresh graduated fisioterapi dengan seorang pasien osteoarthritis dihari yang sama. Analisis item-per-item dilakukan kepada rater sebelum dilakukannya pemeriksaan kepada pasien. Reliabilitas inter-rater diukur menggunakan fleiss kappa (κ). Indek WOMAC yang terdiri dari 5 pertanyaan yang menanyakan keluhan nyeri, 2 pertanyaan tentang kekakuan dan 17 pertanyaan tentang kesulitan dalam melakukan aktifitas keseharian. Hasil uji statistic mengungkapkan bahwa uji inter-rater reliability untuk WOMAC adalah fair agreement, yang berarti klasifikasi kesepakatan antar rater dikatakan belum cukup adekuat untuk digunakan pada fresh graduated fisioterapi.
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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 | 0.041 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".