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Record W2177301669 · doi:10.5535/arm.2015.39.4.616

The Dose-Related Effects of Extracorporeal Shock Wave Therapy for Knee Osteoarthritis

2015· article· en· W2177301669 on OpenAlexaboutno aff
Jin‐Hong Kim, Jayoung Kim, Cheol-Min Choi, June-Kyung Lee, Hoi-Sung Kee, Kwang-Ik Jung, Seo-Ra Yoon

Bibliographic record

VenueAnnals of Rehabilitation Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineExtracorporeal shock wave therapyOsteoarthritisWOMACExtracorporeal shockwave therapyExtracorporealPhysical therapyVisual analogue scaleSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the dose-related effects of extracorporeal shock wave therapy (ESWT) for knee osteoarthritis. METHODS: Seventy-five subjects were recruited, 60 of which met the inclusion criteria. The patients were randomly classified into two groups: group L, which was a low-energy group (n=30; 1,000 shocks/session; energy flux density [EFD], 0.040 mJ/mm(2)) and group M, which was a medium-energy group (n=30; 1,000 shocks/session; EFD, 0.093 mJ/mm(2)). For each group, 1,000 shock waves were delivered to the medial tibial plateau area, once a week, for 3 weeks. The main outcome measures were the visual analogue scale (VAS), the Roles and Maudsley (RM) score, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, and the Lequesne index. Each assessment was performed at the baseline and at 1, 4, and 12 weeks after ESWT. RESULTS: In both groups, the VAS, the RM and WOMAC scores, and the Lequesne index were significantly improved over time (p<0.001), and group M showed greater improvement over group L at the 1, 4 and 12 weeks assessments. CONCLUSION: In this study, medium-energy group (group M) showed greater improvement in regard to relieving pain and restoring functional outcome than the low-energy group (group L). Therefore, EFD can be considered to have significant influence when treating with ESWT for knee osteoarthritis.

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)0.002
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 score0.948
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.042
GPT teacher head0.337
Teacher spread0.296 · 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

Citations74
Published2015
Admission routes1
Has abstractyes

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