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Record W2521792168 · doi:10.13045/acupunct.2016043

Miniscalpel Acupuncture Treatment on a Knee Degenerative Osteoarthritis Patient, Who does not Responded to Acupuncture Treatment<sup>※</sup>

2016· article· en· W2521792168 on OpenAlexaboutno aff
Mu Seob Park, Se Jung Oh, Jung Hee Lee, Seung Ah Jun, Han Gong, Seong Hun Choi, Min Hwangbo, Hyun‐Jong Lee, Kim Jae Soo

Bibliographic record

VenueThe Acupuncture · 2016
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
FundersMinistry of Health and Welfare
KeywordsAcupunctureWOMACMedicineOsteoarthritisVisual analogue scalePhysical therapyAcupuncture therapyMcGill Pain QuestionnaireTherapeutic effectRange of motionSurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

Objectives : This study was done to show the clinical effect of miniscalpel acupuncture treatment on osteoarthritis of the knee which is refractory to acupuncture treatment. Methods : A patient was treated with acupuncture for three weeks, non-treated(wash out period) for two weeks, and treated with miniscalpel acupuncture for three weeks. The effect of treatments were measured with Visual Analogue Scale(VAS), Range of Motion(ROM), Short form McGill pain questionnaire(SF-MPQ), Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC). Results : During the three weeks of acupuncture treatment, VAS, SF-MPQ and WOMAC improved, but after two weeks of the wash out period each score worsened. During the three weeks of miniscalpel acupuncture VAS, SF-MPQ, and WOMAC improved while ROM improved remarkably. Conclusion : These results suggest that miniscalpel acupuncture might be a therapeutic option for knee degenerative osteoarthritis patients who does not responded to acupuncture treatment.

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: Case report · 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)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.039
GPT teacher head0.334
Teacher spread0.295 · 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 designCase report
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

Citations9
Published2016
Admission routes1
Has abstractyes

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