Effect of Korean Medicine Treatment on Patients with Postherpetic Neuralgia: A Retrospective Chart Review
Bibliographic record
Abstract
Evidence regarding Korean medicine treatment (KMT) for neuropathic pain is lacking. We aimed to identify the effects of integrative KMT in patients with postherpetic neuralgia (PHN). We retrospectively analyzed the electronic medical records of patients with PHN who received KMT at Kyung Hee University Korean Medicine Hospital between August 2021 and July 2022. We evaluated the effects of KMT—comprising acupuncture, pharmacopuncture, herbal medicine, cupping, and moxibustion—on pain intensity using the numerical rating scale (NRS), Short-Form McGill Pain Questionnaire (SF-MPQ), Hospital Anxiety and Depression Scale–Anxiety (HADS-A), Hospital Anxiety and Depression Scale–Depression (HADS-D), Daily Sleep Interference Scale (DSIS), Fatigue Severity Scale (FSS), and EuroQol-5D. Among 53 patients with PHN, 13 were included. The NRS score for worst pain over 1 week decreased from 6.54 ± 0.64 at baseline to 3.85 ± 0.63 at 8 weeks (41% reduction, p < 0.01), while that for average pain over 1 week decreased from 4.93 ± 0.67 at baseline to 3.08 ± 0.46 at 8 weeks (37% reduction, p < 0.01). From baseline to 8 weeks, there were significant reductions in the SF-MPQ, HADS-A, FSS, and EuroQol-5D scores. No adverse events were reported after KMT. Therefore, KMT may be an effective treatment option for patients with PHN.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".