MétaCan
Menu
Back to cohort
Record W4414023794 · doi:10.62347/didz9909

Combined trigger point acupuncture knife and traditional Chinese medicine split-tendon massage for shoulder periarthritis: improved function and quality of life

2025· article· en· W4414023794 on OpenAlexaboutno aff
Shijian Wang

Bibliographic record

VenueAmerican Journal of Translational Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMyofascial pain diagnosis and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMassageAcupunctureMedicineTraditional Chinese medicineTendonPhysical therapyAcupuncture pointQuality (philosophy)Physical medicine and rehabilitationAlternative medicineSurgeryPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare the effectiveness of combining trigger point acupuncture knife (TPAK) with Traditional Chinese Medicine (TCM) split-tendon massage therapy versus TPAK alone. METHODS: A retrospective study included 237 patients diagnosed with shoulder periarthritis. Of these, 114 patients received only TPAK therapy (TPAK Group), while 123 patients underwent a combination of TPAK and TCM split-tendon massage therapy (TPAK + TCM Group). Shoulder function was assessed using the Constant-Murley Score (CMS) and shoulder range of motion (ROM). Pain levels were evaluated using the Short-Form McGill Pain Questionnaire (SF-MPQ). Psychological status, sleep quality, and overall quality of life were measured using the WHOQOL-BREF questionnaire, both before and 3 months after treatment. RESULTS: < 0.05), compared to the TPAK group. CONCLUSION: Combining TPAK with TCM split-tendon massage proved more effective than TPAK alone in treating shoulder periarthritis, improving function, reducing pain, enhancing psychological well-being, and improving sleep quality.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0020.000

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 head0.048
GPT teacher head0.376
Teacher spread0.328 · 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 designNon-randomized trial
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

Explore more

Same venueAmerican Journal of Translational ResearchSame topicMyofascial pain diagnosis and treatmentFrench-language works237,207