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Record W4411189211 · doi:10.1007/s10067-025-07497-7

Clinical efficacy of fire-needle warming therapy in the treatment of knee osteoarthritis of cold-dampness type and its effect on serum IL-1β and MMP-3

2025· article· en· W4411189211 on OpenAlexaboutno aff
Tanshu Liu, Yuan Zeng, Binfu Que, Y. K. Tang, Wenbao Wu, Rui Qiu

Bibliographic record

VenueClinical Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersNatural Science Foundation of Fujian Province
KeywordsMedicineOsteoarthritisRheumatologyInternal medicinePathologyAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aims to explore the clinical efficacy of fire-needle warming therapy in managing knee osteoarthritis attributed to cold-dampness patterns. Specifically, it evaluates the therapy's influence on interleukin-1β (IL-1β) serum concentrations and matrix metalloproteinase-3 (MMP-3). METHODS: A retrospective analysis was conducted on 80 patients treated for knee osteoarthritis from September 2023 to February 2024. Patients were divided into two groups: 40 received electroacupuncture, and 40 received fire acupuncture combined with warming techniques. The primary outcome measures included treatment efficacy, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, compare the changes in IL-1β and MMP-3 levels in serum and synovial fluid between two groups, and compare the total knee joint X-ray scores between the two groups: pain levels and knee joint function. Adverse reactions were also recorded. RESULTS: The fire acupuncture group had a significantly higher treatment efficacy rate (92.5%) compared to the electroacupuncture group (75%, P < 0.05). Both groups showed significant reductions in WOMAC scores post-treatment, with a greater reduction in the fire acupuncture group (P < 0.05). Serum levels of IL-1β and MMP-3 decreased significantly in both groups, with a more pronounced decrease in the fire acupuncture group (P < 0.05). After 4 weeks of intervention and 8 weeks of follow-up, the levels of IL-1β and MMP-3 in the synovial fluid of the fire needle group and the electroacupuncture group were significantly reduced (P < 0.05), and the fire needle group was significantly lower than the electroacupuncture group (P < 0.05). Pain and knee function scores improved significantly in both groups, with the fire acupuncture group showing greater improvements (P < 0.05). After 4 weeks of intervention and 8 weeks of follow-up, the total joint X-ray scores of the fire needle group and the electroacupuncture group were significantly reduced (P < 0.05), and the fire needle group was significantly lower than the electroacupuncture group (P < 0.05). The adverse reaction rate was lower in the fire acupuncture group (5%) compared to the electroacupuncture group (20%, P < 0.05). CONCLUSIONS: Fire-needle warming therapy demonstrates significant clinical efficacy in treating knee osteoarthritis of the cold-dampness type. It effectively reduces inflammation and pain and improves knee function, suggesting its potential for broader clinical application. Further research is recommended to confirm these findings. Key Points • Fire needle acupuncture for knee osteoarthritis: Fire needle acupuncture is highly effective in treating cold-dampness arthralgia-type knee osteoarthritis by promoting blood circulation, enhancing metabolism, and reducing inflammation. • Clinical and biochemical outcomes: Patients receiving fire needle acupuncture showed significantly better clinical outcomes, including lower pain scores, improved knee function, and decreased serum levels of IL-1β and MMP-3 compared to those receiving electroacupuncture. • Safety and efficacy: Fire needle acupuncture provides superior therapeutic effects and results in fewer adverse reactions compared to electroacupuncture, highlighting its safety and efficacy in clinical practice.

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)0.001
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 score0.536
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.036
GPT teacher head0.371
Teacher spread0.335 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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