Studi Eksperimental Efektivitas Pijat dengan Minyak Esensial Cengkeh terhadap Status Fungsional Pasien Osteoartritis
Bibliographic record
Abstract
Pengobatan konvensional seperti NSAID sering digunakan untuk mengontrol nyeri dan inflamasi pasien osteoartritis (OA). Namun terkadang obat tersebut tidak efektif. Kombinasi pengobatan konvensional dan pelayanan kesehatan tradisional dirasa mampu mengatasi masalah tersebut. Tujuan penelitian ini adalah untuk mengetahui efektivitas pijat dengan minyak esensial cengkeh terhadap status fungsional pasien OA. Metode yang digunakan adalah quasi experimental-nonequivalent control group design pada 40 pasien OA yang berusia 46-84 tahun dengan skor VAS>4. Pada kelompok intervensi, pasien OA diberikan kombinasi terapi konvensional dan pijat dengan minyak esensial cengkeh. Sebaliknya pada kelompok kontrol pasien OA diberikan kombinasi terapi konvensional dan pijat dengan minyak kelapa. Penilaian efektivitas dilakukan pada hari ke-0, 1, 2 dan 3 pengobatan dengan kuisoner WOMAC (Western Ontario and McMaster Universities Osteoarthritis index). Status fungsional pasien digambarkan dalam empat skor outcome yakni skor total WOMAC, intensitas nyeri, kekakuan, dan fungsi fisik pasien. Hasil penelitian menunjukkan bahwa pijat dengan minyak esensial cengkeh signifikan meningkatkan status fungsional pasien dengan nilai rata-rata skor total WOMAC (21,30 ± 3,36; p=0,02), nyeri (3,80 ± 1,01; p=0,00), kekakuan (1,85 ± 0,75; p=0,00) dan domain fungsi fisik (15,65 ± 2,54; p=0,00) di hari ketiga pengobatan. Sebaliknya kelompok kontrol hanya signifikan menurunkan intensitas nyeri (7,60 ± 1,73; p=0,00) yang dialami pasien. Jadi kombinasi pengobatan konvensional dan pijat dengan minyak esensial cengkeh meningkatkan status fungsional pasien OA.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.015 | 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".