Hubungan Usia, Jenis Kelamin, dan Indeks Massa Tubuh Terhadap Derajat Keparahan Osteoarthritis Genu di RSUD Meuraxa Kota Banda Aceh
Bibliographic record
Abstract
Osteoartritis genu merupakan penyakit degeneratif yang terjadi pada sendi lutut akibat adanya abrasi pada tulang rawan sendi dan pembentukan tulang baru di permukaan persendian. Seiring bertambahnya usia, kapasitas regeneratif tulang rawan menurun, aktivitas sel kondrosit melemah dan terjadi perubahan komposisi matriks ekstraseluler yang menyebabkan berkurangnya elastisitas serta ketahanan sendi terhadap tekanan mekanik. Penelitian ini bertujuan untuk mengetahui hubungan antara usia, jenis kelamin, dan indeks massa tubuh (IMT) terhadap derajat keparahan OA genu di RSUD Meuraxa Kota Banda Aceh. Penelitian ini menggunakan desain analitik observasional dengan pendekatan cross-sectional dan melibatkan 50 responden yang dipilih secara accidental sampling. Data dikumpulkan melalui kuesioner Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) yang mengukur aspek nyeri, kekakuan, dan fungsi fisik. Analisa data menggunakan uji univariat dan uji bivariat. Hasil uji univariat menunjukkan distribusi frekuensi OA genu berdasarkan usia paling banyak pada usia >60 tahun (66%), jenis kelamin paling banyak pada Perempuan (78%), IMT paling banyak dengan obesitas (54%) dan derajat keparahan paling banyak yaitu derajat berat (66%). Hasil uji bivariat menggunakan uji statistik chi-square. Hasil penelitian menunjukkan terdapat hubungan yang signifikan antara usia (p-value = 0,003), jenis kelamin (p-value = 0,042), dan indeks massa tubuh (p-value = 0,014) terhadap derajat keparahan OA genu.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".