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Record W3118375930 · doi:10.26452/ijrps.v11i4.3840

The Comparison of Anti-inflammatory and Analgesic Effect of Diclofenac Alone with Combined Treatment with Diclofenac and Serratiopeptidase in Patients having Osteoarthritis of Knee Joint

2020· article· en· W3118375930 on OpenAlexaboutno aff
Chitra Khanwelkar, Kartik Peethambaran, Sujata Jadhav

Bibliographic record

VenueInternational Journal of Research in Pharmaceutical Sciences · 2020
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsnot available
Fundersnot available
KeywordsDiclofenacOsteoarthritisAnalgesicMedicineVisual analogue scaleWOMACAnesthesiaAlternative medicine

Abstract

fetched live from OpenAlex

Oral proteolytic enzymes like serratiopeptidase are very commonly used by clinicians either alone or in combination with non-steroidal anti-inflammatory drugs for analgesia and anti-inflammatory purpose. As the activity of these drugs is not proved in trials, and they are not listed in any country's official pharmacopoeia, it was planned to study their effect in osteoarthritis patients. Two groups (n= 30 each) of diagnosed knee osteoarthritis patients, were treated with diclofenac 50 mg twice a day (BID) and serratiopeptidase 10mg three times a day (TID) + Diclofenac 50 mg BID for two weeks. The pain and difficulty in daily activities were assessed by Visual Analogue Scale (VAS) and Western Ontario and Mc Masters Universities Osteoarthritis (WOMAC OA) index scale before and after the treatment. Highly significant improvement in both scales was seen in both groups. There was no statistically significant difference in the improvements found in both groups. Addition of serratiopeptidase has not potentiated analgesic and anti-inflammatory effects of diclofenac. Thus, the analgesic and anti-inflammatory efficacy of serratiopeptidase are not proved.

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)0.000
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 score0.218
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
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 head0.059
GPT teacher head0.405
Teacher spread0.346 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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