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Record W2770424675 · doi:10.5535/arm.2017.41.5.828

Effect of Extracorporeal Shockwave Therapy Versus Intra-articular Injections of Hyaluronic Acid for the Treatment of Knee Osteoarthritis

2017· article· en· W2770424675 on OpenAlexaboutno aff
Junekyung Lee, Bong-Yeon Lee, Woo-Yong Shin, Min-Ji An, Kwang-Ik Jung, Seo‐Ra Yoon

Bibliographic record

VenueAnnals of Rehabilitation Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHyaluronic acidExtracorporeal shockwave therapyOsteoarthritisViscosupplementationArticular cartilageExtracorporealPhysical therapyIntra articularSurgeryPhysical medicine and rehabilitationAlternative medicinePathologyAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate and compare the effects and outcomes of extracorporeal shock wave therapy (ESWT) and intra-articular injections of hyaluronic acid (HA) in patients with knee osteoarthritis (OA). METHODS: energy. The HA group was administered intra-articular HA once a week for 3 weeks with a 1-week interval between each treatment. The results were measured with the visual analogue scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Lequesne index, 40-m fast-paced walk test, and stair-climb test (SCT). A baseline for each test was measured before treatment and then the effects of the treatments were measured by each test at 1 and 3 months after treatment. RESULTS: In both groups, the scores of the VAS, WOMAC, Lequesne index, 40-m fast-paced walk test, and SCT were significantly improved in a time-dependent manner (p<0.01). There were no statistically significant differences measured at 1 and 3 months after treatment between the two groups (p>0.05). CONCLUSION: The ESWT can be an alternative treatment to reduce pain and improve physical functions in patients with knee OA.

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)0.000
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 score0.908
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.049
GPT teacher head0.387
Teacher spread0.339 · 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

Citations35
Published2017
Admission routes1
Has abstractyes

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