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Record W2382238690

Cl inical Observation of Tiaojitongdu Acupuncture Therapy on Nonspecific Low Back Pain

2014· article· en· W2382238690 on OpenAlexaboutno aff
Huang We

Bibliographic record

VenueJournal of Emergency in Traditional Chinese Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAcupunctureAcupuncture therapyPhysical therapyAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective: To investigate the curative and analgesic effect of Tiaojitongdu acupuncture therapy treating nonspecific low back pain. Methods: 100 patients were randomly divided into 2 groups: therapy group Tiaojitongdu acupuncture therapy) and control group(routine acupuncture therapy). After 3 periods of treatment,curative effect,half a year relapse rate and the change of scores in SF-MPQ(short-form of Mcgill pain questionnaire) were evualted. Results: Compared with routine acupuncture therapy,Tiaojitongdu acupuncture therapy showed better curative effect(P 0.05),which reduced pain(P 0.05)and prevented relapse rate more effectively. Conclusion: Tiaojitongdu acupuncture therapy is effective in treating nonspecific low back pain which deserves to further study and promotion.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.190
GPT teacher head0.409
Teacher spread0.219 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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