Long-term follow-up of inpatients with meniscus tears who received integrative Korean medicine treatment: A retrospective analysis and follow-up survey
Bibliographic record
Abstract
Evidence regarding the use of Korean medicine (KM) for the conservative treatment of meniscus tears remains lacking. We aimed to evaluate clinical effectiveness and long-term follow-up outcomes in patients undergoing integrative KM treatment for meniscus tears. We analyzed the electronic medical records (EMRs) of 86 patients with meniscus tears and administered a follow-up survey. Patients treated at 1 of 4 KM hospitals between June 1, 2015, and June 30, 2020, were reviewed. KM treatment comprised herbal medicine, acupuncture, pharmacopuncture, bee venom pharmacopuncture, Chuna therapy, and KM physiotherapy. The primary outcome was the numeric rating scale (NRS) score for knee pain; secondary outcomes were the Western Ontario and McMaster Universities Arthritis Index (WOMAC), EuroQol 5-dimension (EQ-5D) score, range of motion, and patient global impression of change. The NRS for knee pain was reduced by an average of 2.49 (95% confidence interval [CI]: 2.03-2.95) at discharge and 1.97 (95% CI: 2.03-2.95) at follow-up. The WOMAC decreased by an average of 15.52 (95% CI: 10.14-20.89) during hospital stay and 30.72 (95% CI: 24.58-36.87) at follow-up. The EQ-5D score increased by an average of 0.06 (95% CI: -0.14 to 0.02) at discharge and 0.19 (95% CI: -0.29 to -0.09) at follow up. KM treatment effectively reduced knee pain, improved knee joint function, and enhanced the quality of life in patients with a meniscus tear for a relatively long period after treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".