Pain Treatment in Primary Care Through Eight Constitution Medicine: A Retrospective Real-World Study from South Korea
Bibliographic record
Abstract
Background and Objectives: Musculoskeletal pain is a global public health issue. Eight Constitution Medicine (ECM), a type of East Asian Traditional Medicine, offers personalized, minimally invasive treatment through Eight Constitution Acupuncture (ECA) and Eight Constitution Lifestyle Intervention (ECLI). Despite its clinical use, scientific evidence supporting ECM’s effectiveness remains limited. This study aimed to evaluate the effectiveness in treating musculoskeletal pain in primary care settings. Materials and Methods: This retrospective study analyzed medical records from three ECM clinics (Gangnam-Shingwang, Yeson, and Yebon) between January 2018 and August 2023. A total of 163 patients were included, with 44 providing follow-up data. Pain intensity, quality of life, and functional outcomes were assessed using validated instruments including the PainDETECT questionnaire, Korean Cancer Pain Assessment Tool (KCPAT) somatic pain scores, EuroQol 5-Dimension 5-Level (EQ-5D-5L), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Oswestry Disability Index (ODI), Neck Disability Index (NDI), and Shoulder Pain and Disability Index (SPADI). Pre- and post-treatment scores were statistically analyzed. Results: Significant decreases were observed in KCPAT somatic pain scores (11.77 ± 4.77 to 9.77 ± 5.32) and significant improvements in EQ-5D-5L scores (0.74 ± 0.12 to 0.80 ± 0.07). WOMAC and ODI scores also showed significant improvements. However, the changes in the NDI, SPADI, and PainDETECT scores were not statistically significant. No adverse events were reported. Conclusions: ECM, through ECA and ECLI, may offer effective personalized treatment for musculoskeletal pain, improving both pain intensity and quality of life. Despite its small sample size and retrospective design, this study offers valuable preliminary evidence for ECM. Further large-scale prospective studies are needed to confirm these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".