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Record W4293222526 · doi:10.13045/jar.2022.00066

The Effects of Korean Medicine Treatment for Meniscus Tears: A Retrospective Chart Review

2022· article· en· W4293222526 on OpenAlexaboutno aff
Ji-won Park, Myung-In Jeong, Wu-Jin Jeong, Hae-Won Hong, Ji-eun Koo, Jun-Hyo Bae, Su-jin Kim, Ji-Eun Bae

Bibliographic record

VenueJournal of Acupuncture Research · 2022
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisTearsAcupunctureMeniscusQuality of life (healthcare)Physical therapyEtiologyInternal medicineSurgeryIncidence (geometry)Alternative medicinePathology

Abstract

fetched live from OpenAlex

Changes in symptoms and dysfunction related to meniscus tears following the use of Korean medicine for ≥ 4 days were studied. The medical charts of 53 cases of diagnosed meniscus tears (magnetic resonance imaging) with an admission Numeric Rating Scale (NRS) score ≥ 4, between 2017 and 2022 were retrospectively reviewed. Treatments included acupuncture, pharmacopuncture, herbal treatment, Chuna therapy, and physiotherapy. The NRS, Western Ontario and McMaster Universities Osteoarthritis Index, and European Quality of Life 5 Dimensions were performed at admission and discharge. There were 42 females and 11 males in this study. Patients were more likely to be in their 60s (38.18%), have an unknown etiology (81.13%), and have complex tears (50.94%). After receiving a combination of alternative Korean medicine during hospitalization, the mean NRS score improved from 6.82 ± 1.19 to 3.66 ± 1.83 (p < 0.001), the Western Ontario and McMaster Universities Osteoarthritis Index score improved from 46.47 ± 20.99 to 37.98 ± 19.23 (p < 0.001), and the mean European Quality of Life Five Dimensions score improved from 0.61 ± 0.18 to 0.68 ± 0.14 (p < 0.001) after treatment. These results suggest that Korean medicine treatment of meniscus tears alleviated pain and improved physical function.

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)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.684
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.115
GPT teacher head0.506
Teacher spread0.390 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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