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Record W4380862978 · doi:10.13045/jar.2023.00031

Drug-Induced Dyskinesia Treated with Korean Medicine: A Case Report

2023· article· en· W4380862978 on OpenAlexaboutno aff
Soo Min Ryu, Jung Won Byun, You Jin Heo, Eun Yong Lee, Cham Kyul Lee, Na Young Jo, Jeong Du Roh

Bibliographic record

VenueJournal of Acupuncture Research · 2023
Typearticle
Languageen
FieldMedicine
TopicPathogenesis and Treatment of Hiccups
Canadian institutionsnot available
Fundersnot available
KeywordsDyskinesiaDrugMedicinePharmacologyInternal medicineParkinson's diseaseDisease

Abstract

fetched live from OpenAlex

Drug-induced dyskinesia is an involuntary muscle movement caused by various dopamine receptor-blocking drug exposure, such as antipsychotics, antidepressants, and antiemetics. Causative drug removal is the main treatment for drug-induced dyskinesia whenever possible because its pathophysiology lacks a universally accepted mechanism; however, the symptoms can persist for years or decades in many patients even after causative drug removal. Herein, we present a case of drug-induced dyskinesia in a 61-year-old female patient who consumed medication for approximately 10 years for her depression, anxiety, and insomnia. Cervical and facial dyskinesia was suggested to be related to perphenazine and levosulpiride administration. The patient received acupuncture, pharmacopuncture, herbal medicine, and chuna treatment for 81 days during hospitalization. The symptoms were evaluated using the Abnormal Involuntary Movement Scale, Toronto Western Spasmodic Torticollis Rating Scale, Tsui’s score, and Numeric Rating Scale, which revealed remarkable improvement, suggesting the effectiveness of combined Korean medicine for drug-induced dyskinesia.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.110
GPT teacher head0.423
Teacher spread0.313 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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