Efektivitas Terapi Adjuvan Injeksi Platelet-Rich Plasma pada Osteoarthritis Sendi Lutut yang mendapatkan NSAIDs
Bibliographic record
Abstract
Osteoartritis (OA) memengaruhi 303 juta orang di dunia dan menjadi salah satu masalah kesehatan yang harus diwaspadai. Terapi penyembuhan OA secara total belum ada. Platelet-rich plasma (PRP) saat ini menjadi target peneliti untuk penanganan pasien OA. Tujuan: Mengetahui efektivitas terapi adjuvan injeksi PRP pada pasien OA sendi lutut yang mendapatkan NSAIDs. Metode: Studi observasional dengan pendekatan cohort retrospektif dilakukan pada pasien osteoartritis sendi lutut di Rumah Sakit Jember Klinik dan Rumah Sakit Daerah Balung. Jumlah sampel yang didapatkan sebanyak 16 pasien. Penelitian ini membandingkan terapi non-steroidal anti-inflammatory drugs (NSAID) tunggal dan NSAID dengan adjuvan PRP. Evaluasi terapi dengan menilai perubahan derajat keparahan gambaran klinis menggunakan kuesioner Western Ontario and Mcmaster Universities Osteoarthritis Index (WOMAC). Uji Fisher’s exact digunakan untuk membandingkan efektivitas terapi. Hasil: terdapat perubahan derajat keparahan gambaran klinis dari 16 sampel pasien. Sebanyak 11 pasien mengalami penurunan derajat keparahan gambaran klinis, dengan rincian 8 pasien (72,7%) dari kelompok terapi NSAIDs dengan adjuvan PRP dan 3 pasien (27,3%) dari kelompok pasien OA yang mendapatkan NSAIDs tanpa adjuvan PRP. Simpulan: Terapi adjuvan injeksi PRP pada pasien OA yang mendapatkan NSAIDs lebih efektif memperbaiki derajat keparahan gambaran klinis penyakit dibandingkan dengan pasien OA yang mendapatkan NSAIDs tanpa adjuvan injeksi PRP.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".