Effects of topical Persian medicine <i>Amygdalus communis</i> L. var. Amara kernel oil on the symptoms of knee osteoarthritis: a randomized, triple-blind, active, and placebo-controlled clinical trial
Bibliographic record
Abstract
Background:The Amygdalus communis L. var.Amara (bitter almond) kernel oil (ACKO) is used topically for palliation of musculoskeletal and joint pains in the Traditional Persian Medicine.Also, it had anti-inflammatory effects in experimental studies.Objective: Evaluation of the efficacy and safety of ACKO in the symptomatic treatment of knee osteoarthritis.Methods: One hundred and fifty six patients were equally randomized to apply ACKO, diclofenac, or placebo to their knees every 8 hours for 1 month.Fifty two, 50, and 51 patients in the ACKO, diclofenac, and placebo groups, respectively, finished the trial.At the trial's start and end, the symptoms were assessed using the WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) questionnaire.Also, hematological, and liver and kidney function tests were performed.Results: Both ACKO and diclofenac reduced the symptoms significantly more than the placebo (P ˂ 0.001).The percent changes of the WOMAC pain and stiffness scores in the ACKO group were similar to the diclofenac group while the percent changes of the WOMAC function and total scores in the ACKO group were less than the diclofenac group (P ˂ 0.001).ACKO and diclofenac had no significant effect on the blood tests.Moreover, no adverse effect was identified.Conclusions: Topical ACKO and diclofenac are safe, and superior to placebo in reducing the symptoms of OA.While ACKO is similar to diclofenac in alleviating pain and stiffness, ACKO is less effective than diclofenac in improving the WOMAC total and function scores.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".