Effect of Moringa oleifera Extract on Inflammatory Status in Cancer Patients with Aromatase-Induced Arthralgia
Bibliographic record
Abstract
BACKGROUND: Aromatase inhibitor therapy is commonly used for breast cancer patients with characteristics of positive estrogen and progesterone receptors test without metastases. Thus, this kind of therapy generally gives side effects of aromatase-induced arthralgia (AIA). Moringa oleifera has a strong anti-inflammatory substance that has the potential to reduce inflammation and pain in a patient with AIA. AIM: This study aims to assess the effect of M. oleifera extract administration on pain response and inflammatory status in breast cancer with aromatase inhibitor-induced arthralgia patients. METHODS: Forty-two patients breast cancer patients with estrogen and progesterone receptor-positive in Dr. Kariadi General Hospital were assessed for pain response and inflammatory status before and after the treatment with M. oleifera leaf extract for one month. Assessment of pain response is using the Australian Canadian osteoarthritis hand index (AUSCAN) questionnaire and inflammation is measured by ANA serum level. This study is experimental with two parallel pre-test and post-test group. RESULTS: In the treatment group, there was a significant decrease of the AUSCAN score 13.5 ± 5.11 (p ≤ 0.001), while in the control group, there was an increase in the AUSCAN score 2.7 ± 4.96 (p = 0.022). In the measurement of ANA serum level, a significant decrease of the treatment group found 0.3 ± 0.40 (p ≤ 0.001). CONCLUSIONS: Moringa oleifera extract can help reduce pain response and inflammatory status of patients with chronic inflammation as an additional therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".