Effect of Jujube/Frankincense as supportive therapy to alleviate the pain with knee osteoarthritis
Bibliographic record
Abstract
Zizyphus jujuba is a thorny Rhamnaceous plant found throughout Europe and Southeast Asia. It possesses medicinal properties and is utilized in traditional medicine. Frankincense is recognized for its analgesic properties. The purpose of this study was to determine the anti-nociceptive efficacy of Z. jujuba and Frankincense in assessing their effect on pain associated with knee osteoarthritis. This research included 46 people ranging in age from 50 to 70 years. They were placed into two groups: test and control. Z. jujuba and Frankincense pills were made, and participants were given a Persian-validated Western Ontario and McMaster Universities (WOMAC), and visual analogue score (VAS) questionnaire. The variables were evaluated before and after the intervention. In the test group, there was a considerable decrease in pain VAS score following the administration of dosages of Z. jujuba/Frankincense pills. The analgesic efficacy of the aforementioned herbal medicines under study was significant after taking mixed jujube/frankincense/aloe vera tablets at doses of 250 mg/100 mg/25 mg, respectively. The combined standardized mean difference for the WOMAC, were 14.56 ± 3.2, 13.9 ± 3.50, and 13.33 ± 4.09, for the before, one-week, and one-month follow-ups, respectively. WOMAC and VAS scores were different between “before intervention” as well as both “one week”, and “one-month” follow-ups. In summary, pain reduced in study group compared to their respective baseline (VAS and WOMAC outcome measures). These findings confirmed that Z. jujube, and frankincense supplementation effectively reduce pain and improve functional outcomes in OA patients, supporting their use as a complementary treatment alongside standard therapies.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".