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Record W4387058101 · doi:10.26689/bas.v1i2.5223

Comparison of the Clinical Efficacy of Arthroscopic Surgery and Extracorporeal Shock Wave Therapy in the Treatment of Knee Osteoarthritis

2023· article· en· W4387058101 on OpenAlexaboutno aff
Chao Tong

Bibliographic record

VenueBone and Arthrosurgery Science · 2023
Typearticle
Languageen
FieldMedicine
TopicMedical and Biological Ozone Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisWOMACExtracorporeal shock wave therapyExtracorporealMalondialdehydeSurgeryVisual analogue scalePhysical therapyAnesthesiaInternal medicineOxidative stress

Abstract

fetched live from OpenAlex

This paper aims to explore the clinical efficacy of arthroscopic surgery and extracorporeal shock wave therapy in patients with knee osteoarthritis. The research period was from February 2022 to January 2023. A total of 79 patients with knee osteoarthritis were included and divided into the study group (n = 40) and the control group (n = 39) by lottery method using computer software. The patients in the control group were treated with arthroscopic surgery, and the patients in the study group were treated with extracorporeal shock wave therapy. The Lysholm score, visual analogue scale (VAS) score, Western Ontario and McMaster University Osteoarthritis (WOMAC) score, superoxide dismutase (SOD) level, and malondialdehyde (MDA) level were compared between the two groups. After treatment, the Lysholm score of the study group was higher than that of the control group, and the VAS score and WOMAC score were lower than those of the control group (P < 0.05). While the MDA level of the study group was lower than that of the control group, and the SOD level was higher than that of the control group (P < 0.05). Extracorporeal shock wave therapy for patients with knee osteoarthritis can restore joint function, relieve pain, and effectively regulate the level of oxygen free radicals.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
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 head0.195
GPT teacher head0.414
Teacher spread0.219 · 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.

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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