Effect of Ginger-Naproxen on Knee Osteoarthritis: A Clinical Study
Bibliographic record
Abstract
Objective: To evaluate clinical efficacy and safety of ginger with naproxen, in treating knee osteoarthritis.Study design and setting: Randomized clinical trial conducted in medicine department OPD of National Medical Center,Karachi from 21st September 2018 till 31st March 2019.Methodology: This study was conducted on 60 patients of knee osteoarthritis. After written informed consent, the patientswere randomized to two groups. Group A received tablet naproxen 500mg and capsule ginger 550mg, twice daily and groupB was given tablet naproxen 500mg twice daily. Total 53 patients finished the study (group A: n=27 and group B: n=26).Baseline pain (Visual Analogue Scale) and Western Ontario and McMaster Universities Osteoarthritis index (WOMAC)scores were noted at the beginning of study and reassessed after 6 weeks of the intervention. Safety profile of the drugswas assessed by observing adverse effects. Independent t-test was applied to check difference between the two groups.Statistical analysis was performed using SPSS version 23.0. P-value < 0.05 was considered as statistically significant.Results: Before the intervention no significant difference was observed in two groups. However significant difference wasobserved between the groups in pain (p=0.019) and WOMAC (p=0.020) scores after 6 weeks of intervention. Moreoverthere was no significant difference (p=0.914) in occurrence of adverse effects between the two groups at the end of 6 weeksof study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".