The Effect of Korean Medicine Treatment for Hemorrhagic Transformation after Interventional Reperfusion Therapy of an Anterior Circulation Infarction in a Patient with Hemiplegia and Conscious and Cognitive Disorders: A Case Report
Bibliographic record
Abstract
Objectives: The study investigated the effect of Korean medicine treatment on a hemiplegic patient with conscious and cognitive disorders due to hemorrhagic transformation after interventional reperfusion therapy of anterior circulation infarction.Case presentation: The patient was treated with acupuncture, moxibustion, cupping, and herbal medicine in combination with Western medicine and physical therapy. The effects on clinical symptoms were evaluated using the Manual Muscle Test (MMT), Glasgow Coma Scale (GCS), Korean Mini-Mental State Examination (K-MMSE), Korean Nursing Delirium Screening Scale (Korean Nu-DESC), and Modified Bathel Index (MBI). After the treatment, the MMT grade increased from Gr.0-1 to Gr.0-3, the GCS score increased from 10 to 15, the K-MMSE score increased from 8 to 15, the Korean Nu-DESC score decreased from 3 to 1, night delirium disappeared, and the MBI score increased from 13 to 26.Conclusions: Complex Korean medicine treatments were effective for improving the clinical symptoms of hemorrhagic transformation after interventional reperfusion therapy for anterior circulation infarction in a patient with hemiplegia and conscious and cognitive disorders. However, further studies are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".