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Record W3144399827 · doi:10.5535/arm.2018.42.2.374

In Reply: Comment on “Effect of Extracorporeal Shockwave Therapy Versus Intra-articular Injections of Hyaluronic Acid for the Treatment of Knee Osteoarthritis”

2018· article· en· W3144399827 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 · 2018
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineExtracorporeal shockwave therapyHyaluronic acidOsteoarthritisViscosupplementationIntra articularExtracorporealSurgeryPhysical therapyAlternative medicinePathologyAnatomy

Abstract

fetched live from OpenAlex

I appreciate your interest of our study and are grateful for the comment. We evaluated and compared the effects and outcomes of the extracorporeal shock wave therapy and intra-articular injections of hyaluronic acid in patients with knee osteoarthritis using the visual analogue scale, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Lequesne index, 40-meter fast-paced walk test, and stair-climb test. The Osteoarthritis Research Society International recommended performance-based tests to assess the physical function in patients with knee osteoarthritis Therefore, we conducted a 40-m fast-paced walk test and a stair-climb test to further evaluate the patient's physical function. As is correct in your opinion assessment, the gait analysis evaluation is an objective instrumentation to support the effect of the extracorporeal shock wave therapy and the intra-articular injections of hyaluronic acid in patients with knee osteoarthritis. The pathology of the patient's osteoarthritis is commonly degenerative and chronic. We agree that a long-term follow-up study is useful to achieve the management of knee osteoarthritis. We are going to apply it to the following study and will review the results of this follow-up effort.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.044
GPT teacher head0.374
Teacher spread0.330 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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