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Record W4399795877 · doi:10.22246/jikm.2024.45.2.176

A Case Report of Severe Femoral Neuropathy with Motor Weakness and Hypoesthesia Treated by Combined Western-Korean Medicine Treatment

2024· article· en· W4399795877 on OpenAlexaboutno aff
So-min Jung, Seon-uk Jeon, Moon-young Ki, Ye-chae Hwang, Gyeongmuk Kim, an-Gyul Lee, Sang‐Kwan Moon, Woo‐Sang Jung, Seungwon Kwon

Bibliographic record

VenueThe Journal of Internal Korean Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHypoesthesiaMedicineFemoral nerveSurgeryWeaknessMuscle atrophyAtrophyInternal medicine

Abstract

fetched live from OpenAlex

In femoral neuropathy, the femoral nerve is compressed or ischemic. Patients with femoral neuropathy experience muscle atrophy, difficulty flexing the hip joint and extending the knee, decreased sensation of the lower extremities, and loss of patellar tendon reflex. The prognosis of femoral neuropathy is reported to vary, as it takes several days to several months for neurological abnormalities to resolve. We describe a case of a 58-year-old female with a diagnosis of severe femoral neuropathy and complaints of motor weakness and hypoesthesia. The patient underwent combined Western-Korean medicine treatment. The Toronto Clinical Neuropathy Scoring System, Overall Neuropathy Limitations Scale, and Berg Balance Scale were used as evaluation tools during the treatment period. The combined Western-Korean medicine treatment led to a significant improvement in symptoms in this patient with severe femoral neuropathy where the cause was unclear and the prognosis was expected to be poor.

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)0.001
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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