Effects of Galbanum Oil on Patients with Knee Osteoarthritis: A Randomized Controlled Clinical Trial
Bibliographic record
Abstract
Knee osteoarthritis is considered as a major public health issue causing chronic disability worldwide with the increasing number of aging people. Many sufferers with knee osteoarthritis are using complementary and alternative medicine including herbal drug, herbal patch, etc. This research aimed to compare the efficacy of galbanum oil in the management of knee osteoarthritis with diclofenac gel using parallel randomized controlled design. In this randomized controlled trial (RCT), 32 patients with chronic knee pain due to osteoarthritis were randomly allocated into two equal groups. The intervention group received topical galbanum oil and control group received topical diclofenac gel. The drugs were given for 1 month, 3 times per day. Outcomes were assessed using Persian version of Western Ontario McMaster University Osteoarthritis Index (WOMAC) and visual analog scale (VAS). No statistically significant difference was observed between the topical galbanum oil and topical diclofenac gel regarding knee pain, morning stiffness and physical function over the 1- and 2-months follow-up period. It seems that using topical galbanum oil is clinically effective for patients suffering from knee osteoarthritis in order to decrease their pain, morning stiffness and limited function; its effect is comparable with topical diclofenac gel. Considering lesser side effects of galbanum and its herbal base, it may be a better choice compared to topical diclofenac in management of the pain of knee osteoarthritis at least in short term.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".