The Study on Correlation between the KL-Grade and Improvement of Knee Pain Treated by Korean Medicine Therapy
Bibliographic record
Abstract
Objectives The purpose of this study is to compare the effects between the KL-Grade and improvement of knee pain treated by Korean Medicine therapy.Methods 114 patients who received inpatient treatment from July 2014 to May 2017 in the Daejeon Jaseng of Korean Medicine Hospital were divided into 5 groups by the KL-Grade.All patients received a combination of treatment including acupunture, pharmacopunture, herbal medication.They were compared and analyzed on the basis of improvement between measuring Numeric Rating Scale (NRS), Western Ontario and McMaster Universities Arthritis Index (WOMAC Index), EuroQol-5 Dimension Index (EQ-5D Index) as they were hospitalized and as they were discharged.The statistically significance was evaluated by SPSS 23.0 for windows.Results After treatment, KL-Grade 0 group's Numeric Rating Scale (NRS), Western Ontario and McMaster Universities Arthritis Index (WOMAC Index), EuroQol-5 Dimension Index (EQ-5D Index) improvement was 2.02±1.69,7.50±9.67and 0.11±0.15respectively.KL-Grade 1 group's improvement was 2.09±1.23,11.75±13.99and 0.12±0.13respectively.KL-Grade 2 group's improvement was 1.60±1.07and 14.70±14.19respectively.But In this group, EQ-5D Index has decreased by 0.01±0.10.KL-Grade 3 group's improvement was 1.88±1.31,7.81±13.35and 0.13±0.20 respectively (p＜0.034).In the case of KL-Grade 4, the population was not statistically significant (N=2) and therefore excluded from statistical significance.And there was no statistically significance between 4 group's improvement after treatment (p＞0.05). ConclusionsThe above study showed that Korean medicine treatments showed significant therapeutic effects on knee pain and degenerative knee joints, but there was no significant difference in the effectiveness of degenerative arthritis (KL-Grade).(
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".