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Record W4409734422 · doi:10.13702/j.1000-0607.20231059

[The long-term follow-up clinical research of ultrasound-guided warm needle knife in treatment of advanced knee osteoarthritis].

2025· article· en· W4409734422 on OpenAlexaboutno aff
Jing Yin, Sheng Ju, Xiao-Juan Luo, Yi-Xuan Duan, Zhaoqing Zhang, Huijun Chen

Bibliographic record

VenuePubMed · 2025
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicineTerm (time)UltrasoundKnee JointRadiologySurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the long-term efficacy and safety of ultrasound-guided warm needle knife therapy for patients with advanced knee osteoarthritis (KOA). METHODS: A total of 70 advanced KOA patients were recruited from the Department of Pain and Rehabilitation of the Third Hospital of Wuhan from June 2020 to June 2022. They were randomized to a treatment group or a control group in a 1∶1 ratio. Patients in the treatment group received ultrasound guided warm needle knife treatment, while patients in the control group received ultrasound guided radiofrequency treatment of the knee sensory plexus, both for 1 time. The primary outcome was the changes in Western Ontario McMaster University Osteoarthritis Index (WOMAC) scores. Other outcomes included the Visual Analogue Pain Scale (VAS) scores, value of Young's modulus of A-shi points, proportion of patients achieving improvement, and adverse events related to study interventions. RESULTS: <0.05). No adverse events were reported. CONCLUSIONS: Ultrasound guided warm needle knife is effective in improving the function of knee joint and relieving pain in patients with advanced KOA. Further, the long-term efficacy of ultrasound guided warm needle knife for advanced KOA was superior to the ultrasound guided radiofrequency therapy.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.122
GPT teacher head0.440
Teacher spread0.318 · 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 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
Published2025
Admission routes1
Has abstractyes

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