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Record W2909547687 · doi:10.22246/jikm.2018.39.6.1306

A Case Report on a Phantom Limb Pain Patient after Below Knee Amputation using Korean Medicine Treatment

2018· article· en· W2909547687 on OpenAlexaboutno aff
Hyung-bum Seo, Go-eun Bae, Jin Yong Choi, Hee-jeong Seo, So-hyun Shim, Chang-woo Han, Soyeon Kim, Jun‐Yong Choi, Seong-ha Park, Young-ju Yun, Jin-woo Hong, Jung-nam Kwon, In Lee

Bibliographic record

VenueThe Journal of Internal Korean Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsnot available
FundersPusan National University
KeywordsMedicineAmputationPhantom limb painPhantom painPhysical therapyAcupunctureElectroacupuncturePhantom limbRating scalePhysical medicine and rehabilitationSurgeryAlternative medicinePsychology

Abstract

fetched live from OpenAlex

Objectives This study presented the case of a 49-year-old Korean female with phantom limb pain after below right knee amputation and aimed to assess the effectiveness of Korean medicine treatment. Methods The patient was treated with scalp acupuncture, electroacupuncture and herbal medicine. We executed a numerical rating scale (NRS), conducted a global assessment (G/A), administrated a Short-Form McGill Pain Questionnaire 2 (SF-MPQ-2) and measured total daily sleep time to evaluate symptom improvement. Results The patient’s G/A scores decreased from 10 to 2 and SF-MPQ-2 points decreased from 20 to 6 after treatment. The total daily sleep time did not changed due to anxiety. Conclusions This study suggests that Korean medicine treatment could be effective in treating phantom limb pain after amputation. Further studies are needed. Keywords: phantom limb pain, amputation, acupuncture, case report

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 score0.251
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.313
Teacher spread0.283 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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