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Record W2372900978

Clinical Research on Different Interval in Treating the Pain of Cervical Spondylosis with Acupuncture

2011· article· en· W2372900978 on OpenAlexaboutno aff
Fu Wenbin

Bibliographic record

VenueLiaoning zhongyi zazhi · 2011
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCervical spondylosisMedicineAcupunctureMcGill Pain QuestionnairePhysical therapyAnesthesiaAlternative medicineVisual analogue scale
DOInot available

Abstract

fetched live from OpenAlex

Objective:To find out a better interval for treating the pain of cervical spondylosis through the clinical research on different interval in treating the pain of cervical spondylosis with acupuncture.Methods:60 cases were randomly divided into Group 1 and Group 2 equally,treating them with acupuncture every other day and everyday respectively in weekdays.The curative effect was evaluated by NPQ and McGill scale.Results:The effective rate of Group 1 is 78.57% and Group 2 is 74.07%.There is no significant(P0.05)difference.In Group 1,the scores of NPQ and McGill significantly decrease(P0.01) form the beginning to the 6th time and from the 6th time to the end of the course.The scores of NPQ and McGill significantly decrease(P0.01)in Group 2 at the end of the course.At the end of the 2nd week,there is no significant difference(P0.05) between Group 1 and Group 2 for the scores of NPQ and McGill.Conclusion:Treating the pain of cervical spondylosis with acupuncture is a safe and effective method.In addition,treating the pain of cervical spondylosis with acupuncture everyday or every other day for 10 times are both effective.The treatment of every other day is more effective.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.072
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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 integrity0.0000.001
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 head0.237
GPT teacher head0.456
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 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

Citations0
Published2011
Admission routes1
Has abstractyes

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