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Comparative Efficacy and Safety of Benjakul vs. Naproxen in Primary Knee Osteoarthritis: A Multicenter RCT

2025· article· en· W4408876734 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of the Medical Association of Thailand · 2025
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisRandomized controlled trialNaproxenMulticenter studyPhysical therapySurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

Objective: To compare the efficacy and safety of Benjakul (BJK) and Naproxen (NPX) for treating primary knee osteoarthritis (KOA) in a multicenter randomized trial. Materials and Methods: Three hundred fifty participants were randomly assigned to receive either BJK or NPX for four weeks. The primary endpoint was the change in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score. Similarly, secondary endpoints including the 40-meter fast-paced walk test (40mFPWT), Timed Up and Go test (TUG), WOMAC, Knee Injury and Osteoarthritis Outcome Score (KOOS), and the 12-item Short Form Survey (SF-12V2). Statistical analysis revealed comparable improvements across all measures (p≤0.05), confirming treatment equivalence. Safety evaluations were performed through laboratory tests. Results: BJK showed notable improvements in pain reduction and functional outcomes compared to NPX, with significant improvements in VAS, TUG, total WOMAC score, and individual categories of the KOOS score at 14 and 28 days. The adverse event rate was dry lips and throat in 8.2% in the BJK group and 6.5% in the NPX group with abdominal discomfort, and constipation being the most common side effect. Conclusion: BJK demonstrates comparable efficacy and safety to NPX in treating KOA, with no significant safety concerns identified in the clinical trial. This suggests that BJK can be recommended as a natural anti-inflammatory drug for patients suffering from knee osteoarthritis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.232

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.255
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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