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Record W4394840487 · doi:10.1097/js9.0000000000001116

The efficacy of extracorporeal shock wave therapy for knee osteoarthritis : an umbrella review

2024· review· en· W4394840487 on OpenAlexaboutno aff
Peiyuan Tang, Ting Wen, Wenhao Lu, Hongfu Jin, Linyuan Pan, Hengzhen Li, Biyun Zeng, Yang Zhou, Wenfeng Xiao, Yusheng Li

Bibliographic record

VenueInternational Journal of Surgery · 2024
Typereview
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineExtracorporeal shockwave therapyInclusion and exclusion criteriaOsteoarthritisWOMACExtracorporeal shock wave therapyMeta-analysisVisual analogue scaleRange of motionWeb of sciencePhysical therapyInternal medicineSurgeryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: An umbrella review was conducted to compare the effectiveness of extracorporeal shock wave therapy (ESWT) versus non-ESWT in the treatment of knee osteoarthritis (KOA). MATERIALS AND METHODS: Three databases including PubMed, Embase and Web of science were searched up to September 2023. Literature screening, quality evaluation, and data extraction were performed according to inclusion and exclusion criteria. Meta-analysis of outcome indicators was performed using Revman 5.4 software. RESULTS: A total of eight meta-analysis were included in this umbrella review. All meta-analysis were graded against a Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) and scored between 8 and 11. Compared to the sham group, the ESWT group showed better results in WOMAC (Western Ontario and McMaster Universities Arthritis Index) [mean difference (MD)=-2.94, 95% CI: -5.52, -0.37, P=0.03, I²=60%], Visual Analog Scale (VAS) (MD=-2.0, 95% CI: -2.5, -1.5, P<0.01, I²=0%), range of motion (ROM) (MD=17.55, 95% CI: 13.49, 21.61, P<0.00001, I²=0%), and Lequesne index (MD=-2.85, 95% CI: -3.64, -2.07, P<0.00001, I²=48%). CONCLUSION: Based on the results of our analysis, ESWT is now an effective therapy for improving pain and function in patients with KOA.

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: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
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 head0.134
GPT teacher head0.408
Teacher spread0.274 · 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
GenreReview

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

Citations30
Published2024
Admission routes1
Has abstractyes

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