퇴행성 반월상 연골판 파열 환자의 한의학적 치료 효과와 Kellgren-Lawrence-grade, Body Mass Index의 상관성
Bibliographic record
Abstract
Objectives The aim of this study is to observe the effectiveness of complex Korean medicine treatment applied to the patients with degenerative meniscal tear and the correlation among clinical effectiveness, body mass index (BMI) and Kellgren-Lawrence grade (KL-grade). Methods The study participants were 38 patients who had been diagnosed with degenerative meniscal tear. Participants were classified by BMI, KL-grade and treated with acupuncture, electroacupuncture and pharmacopuncture. Clinical outcomes were assessed using Numeric Rating Scale (NRS), Western Ontario and McMaster Universities Arthritis Index (WOMAC Index) and EuroQol-5 Dimension Index (EQ-5D Index). Results Both NRS and WOMAC scores were significantly reduced after treatment (p ＜0.001). The EQ-5D for assessing quality of life showed further improvement (p＜0.05). A statistically significant correlation was observed between the BMI and NRS, EQ-5D. KL-grade was correlated with WOMAC. Conclusions These results show that complex Korean medicine treatment to the patient with degenerative meniscal tear may be effective as a conservative therapy. Further research is required to confirm the effectiveness of Korean medicine treatment. (J Korean Med Rehabil 2018;28(4):71-79)
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".