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Record W2960779739 · doi:10.18535/jmscr/v7i7.44

Evaluation of efficacy of Triamcinolone Acetate Intra Articular Therapy for Knee Osteoarthritis: A Case Control Study

2019· article· en· W2960779739 on OpenAlexaboutno aff
Dr MD. Jaan Basha

Bibliographic record

VenueJournal of Medical Science And clinical Research · 2019
Typearticle
Languageen
FieldMedicine
TopicIntramuscular injections and effects
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisIntra articularTriamcinolone acetonidePhysical therapySurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

Knee osteoarthritis constitutes a public health concern of immense dimensions, and places a burden on society similar to few other conditions.Corticosteroids are used for treatment.The aim of this study is to evaluate efficacy of intra-articular triamcinolone injections for osteoarthritis of the knee.This is a double blind, prospective, randomized, and controlled study conducted at Deccan college of medical sciences, Hyderabad after obtaining permission from the hospital ethics committee.This study includes 60 patients of both sexes aging from 50-70 years.These patients were divided in two groups randomly by the closedenvelope technique.Diagnosis was done using Kellgren and Lawrence scale for osteoarthritis.The efficacy of pain relief in the 2 groups were compared using Visual analog score (VAS), Oxford knee Score and McGill Quality of life score followed upto 6 months.The study group was given 40mg of 1ml triamcinolone acetate and compared with control normal saline.The base line parameter VAS scores (6.03±1.36&6.43±1.02)Oxford knee Score (44.46±4.02& 45.23±5.23)and QOL scores (86.03±4.36 and 85.36±5.32)were comparable.The use of triamcinolone decreased the pain and pain relief was satisfactory to patients and it also improved the Quality of life score up to 6 months.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.187
GPT teacher head0.548
Teacher spread0.361 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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