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Record W2362713945

Clinical Observation of Acupuncture Combined with Massage Manipulation on Treating Facet Joints Disorder

2013· article· en· W2362713945 on OpenAlexaboutno aff
Chen Tianchen

Bibliographic record

VenueJournal of Sichuan of Traditional Chinese Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMassageAcupuncturePhysical therapyClinical efficacyTherapeutic effectSignificant differenceSurgeryInternal medicineAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective: To observe the clinical effect of acupuncture combined with massage manipulation on treating facet joints disorder. Methods: 64 cases were randomly divided into the treatment group and the controlled group,each group was 32 cases. The treatment group was treated by acupuncture combined with massage manipulation,the controlled group was treated by simple massage manipulation as treatment group,2 days 1 time,3 times to be a period of treatment,total treatment 6 times, and in before and after treatment used McGill scale to evaluate. Results: After the treatment,7 cases was cured,22 cases was better,3 cases was invalid in the treatment group,the total effective rate was 90. 6%; 7 cases was cured,20 cases was better, 5 cases was invalid in the controlled group,the total effective rate was 84. 4%. McGill scale evaluation: After the treatment, the PRI,VAS and PPI of both groups of patients were significantly lower( P 0. 001),and the dropped degree had significant difference( P 0. 05). Conclusion: Acupuncture combined with massage manipulation for thoracic spinal with small joint disorders has a certain extent of therapeutic effect and is better than simple massage manipulation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.094
GPT teacher head0.360
Teacher spread0.265 · 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 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

Citations4
Published2013
Admission routes1
Has abstractyes

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