Long-term follow-up of inpatients with traumatic fractures who received integrative Korean Medicine treatment: A retrospective analysis and questionnaire survey study
Bibliographic record
Abstract
Previous studies have reported pain reduction after Korean medicine (KM) treatment in patients with fractures. However, these studies were limited by small sample sizes and short observation periods. To address these limitations, we aimed to analyze the outcomes of patients with traumatic fractures who received integrative KM treatment and investigate their long-term progress through follow-up observations. This study was a retrospective analysis and questionnaire survey conducted at a multi-center inpatient care setting in Korea. A total of 1150 patients who had traumatic fractures and received at least 5-day inpatient care at one of 5 KM hospitals. Finally, 339 patients completed the follow-up survey. The questionnaire survey was administered 3 months post discharge. The primary outcome was the difference in numeric rating scale (NRS) scores at admission and discharge for fracture-related pain. The secondary outcomes were EuroQol 5-Dimension 5-Level (EQ-5D-5L) score, Oswestry Disability Index, Neck Disability Index, Western Ontario and McMaster Universities Arthritis Index, Shoulder Pain and Disability Index, and Patient Global Impression of Change (PGIC) score. The follow-up questionnaire survey included questions on surgery and imaging before admission and after discharge and treatment within the past 3 months. The mean NRS score at follow-up showed a significant decrease of 4.41 points compared with that at admission (P < .001). The mean EQ-5D-5L score at follow-up showed a significant increase of 0.18 points compared with that at admission (P < .05). In the follow-up survey on PGIC, 307 participants (90.56%) were "minimally improved" or better. Integrative KM treatment can help improve pain, functional impairment, and long-term quality of life in patients with traumatic fractures.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".