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Record W4390038028 · doi:10.1186/s12891-023-07072-8

Topical Chinese patent medicines for chronic musculoskeletal pain: systematic review and trial sequential analysis

2023· review· en· W4390038028 on OpenAlexaboutno aff
Kaiqiang Tang, Jigao Sun, Yawei Dong, Zelu Zheng, Rongtian Wang, Na Lin, Weiheng Chen

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2023
Typereview
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaTufts University School of MedicineBeijing University of Chinese MedicineNational Natural Science Foundation of China
KeywordsMedicinePhysical therapyRandomized controlled trialOsteoarthritisWOMACVisual analogue scaleCochrane LibraryChronic painClinical trialRheumatologyPain medicineMeta-analysisMEDLINESystematic reviewAlternative medicineInternal medicineAnesthesiologyAnesthesia

Abstract

fetched live from OpenAlex

PURPOSE: Chronic musculoskeletal pain (CMP) is defined as persistent or recurrent pain that occurs in the joints, musculo-soft tissue, spine or bones for more than three months and is not completely curable. Although topical Chinese patent medicine (CPM) is the most extensively utilized medication in Asia and is widely used for pain management, its efficacy remains controversial. This article presents a systematic review of clinical studies on the therapeutic properties of topical CPM for CMP patients to better inform clinical decision-making and provide additional and safer treatment options for patients with CMP. METHOD: We performed a comprehensive search on PubMed, Cochrane Library, web of science and Chinese databases (CNKI and WanFang data) from 2010 to 2022. In all the studies, knee osteoarthritis, cervical spondylosis, low back pain, and periarthritis of shoulder met the International Pain Association definition of chronic musculoskeletal pain. We included only randomized controlled trials (RCTs) using topical CPM primarily for chronic musculoskeletal pain in adults. To determine the effect of topical CPM on clinical symptoms, we extracted the Visual Analog Scale (VAS, range 0-10) and the Western Ontario and McMaster Universities Arthritis Index pain scores (WOMAC pain, range 0-20), in which the lower the score, the better the results. We also accepted the comprehensive outcome criteria developed by the Chinese National Institute of Rheumatology as an endpoint (total effectiveness rate, range 0-100%, higher score = better outcome), which assesses the overall pain, physical function and wellness. Finally, trial sequential analysis of VAS pain score and total effectiveness rate was performed using TSA software. RESULTS: Twenty-six randomized controlled trials (n = 3180 participants) compared topical CPM with oral Nonsteroidal Anti-inflammatory Drugs (NSAIDs) (n = 15), topical NSAIDs (n = 9), physiotherapy (n = 5), exercise therapy (n = 4), and intra-articular Sodium hyaluronate injection (n = 2). Sixteen studies found that topical CPM was statistically significant in improving CMP pain (measured by VAS pain and Womac pain scores)(p < 0.05), and 12 studies found topical CPMs to be more clinically effective (assessed by ≥ 30% reduction in symptom severity) in treating patients with CMP (p < 0.05). Trial sequential analysis indicates that the current available evidence is robust, and further studies cannot reverse this result. In most of the studies, randomisation, allocation concealment and blinding were not sufficiently described, and no placebo-controlled trials were identified. CONCLUSION: Most studies showed superior analgesic effects of topical CPM over various control treatments, suggesting that topical CPM may be effective for CMP and is an additional, safe and reasonable treatment option. These reported benefits should be validated in higher-quality RCTs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0210.017
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.063
GPT teacher head0.388
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations3
Published2023
Admission routes1
Has abstractyes

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