Bibliographic record
Abstract
Osteoartritis lutut merupakan penyakit degeneratif kronis yang menyebabkan nyeri, kekakuan, dan terbatasnya gerak sendi sehingga terjadi disabilitas dan penurunan kualitas hidup. Prevalensi semakin meningkat dengan peningkatan usia. Terdapat berbagai jenis terapi untuk osteoartritis tetapi hanya sedikit yang dapat memperbaiki derajat degenerasinya. Injeksi intraartikular masih merupakan terapi non bedah yang popular untuk osteoartritis lutut, terutama jika dengan obat oral dan latihan masih dirasakan nyeri. Proloterapi merupakan teknik injeksi regeneratif non bedah yang memasukkan sedikit larutan iritatif ke tempat yang nyeri dan insersi tendon, sendi, ligamen serta daerah sekitar sendi selama beberapa kali pemberian untuk merangsang pertumbuhan sel dan jaringan normal. Agen proloterapi yang sering digunakan adalah dekstrosa dengan konsentrasi berkisar antara 12,5% sampai 25%. Dekstrosa dianggap agen proloterapi yang ideal karena sifatnya yang larut air dan dapat diinjeksikan ke beberapa area dengan aman. Injeksi dekstrosa hipertonik intraartikular dan ekstrartikular dalam tiga sampai lima kali sesi memiliki efek klinis dan statistik yang signifikan dalam perbaikan skor WOMAC (Western Ontario McMaster University Osteoarthritis Index), skala nyeri dan fungsional. Keuntungan proloterapi dekstrosa yaitu aman, murah, dan mudah diberikan serta belum ada penelitian yang melaporkan efek samping jangka panjang maupun permanen.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".