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THE EFFECT OF DIACEREIN AND MELOXICAM COMBINATION VERSUS MELOXICAM ALONE ON PHYSICAL FUNCTION IN PATIENTS WITH KNEE OSTEOARTHRITIS

2018· article· en· W2895178870 on OpenAlexaboutno aff
Ni Made Oka Dwicandra, Made Krisna Adi Jaya

Bibliographic record

VenueAsian Journal of Pharmaceutical and Clinical Research · 2018
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMeloxicamMedicineWOMACOsteoarthritisCombination therapyRandomized controlled trialPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Objective: More than 50% of patients with knee osteoarthritis (OA) had an inadequate pain relief in its management. Combination therapy could be the solution to this problem. The aim of this study was to compare the efficacy of combination therapy of diacerein and meloxicam with meloxicam alone in the patient with knee OA.Methods: A total of 64 knee OA patients were recruited from Rumah Sakit Umum Daerah Dr. Mohammad Soewandhie Surabaya. They were allocated to combination group and single therapy group using randomized controlled trial design. The Western Ontario and McMaster Universities Arthritis Index (WOMAC) physical function questionnaire were assessed in weeks 0–4th. The difference between pre- and post-treatment score and area under the curve (AUC) of WOMAC score were calculated.Results: Combination therapy and single therapy had significant clinical effect with the downregulated score of WOMAC physical function after 4th week (p<0.05). However, there were no differences in AUC of WOMAC physical function score between combination and single therapy.Conclusion: Patient with knee OA could gain beneficial efficacies from combination therapy of diacerein and meloxicam. Studies of longer follow-up time to get the differences in AUC of WOMAC physical function score are needed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.424
Teacher spread0.377 · 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 designOther design
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

Citations4
Published2018
Admission routes1
Has abstractyes

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Same venueAsian Journal of Pharmaceutical and Clinical ResearchSame topicOsteoarthritis Treatment and MechanismsFrench-language works237,207