A Retrospective Chart Review of 114 Patients with Knee Pain at a Korean Medicine Hospital Who Had Been Involved in a Traffic Accident
Bibliographic record
Abstract
Background: This study aimed to investigate the demographic characteristics of patients with knee pain caused by traffic accidents and test the effectiveness of Korean medicine (KM) treatment.Methods: The medical charts of 114 inpatients with knee pain caused by a traffic accident were reviewed from July 1, 2019 to October 31, 2019 at Bucheon Jaseng Hospital of KM. The patients’ demographics including gender, age, period of hospitalization, and type of pharmacopuncture and herbal medicine prescribed were reviewed. The Numeric Rating Scale scores and Western Ontario and McMaster Universities Osteoarthritis Index scores were used to assess subjective knee pain.Results: There were more females (55%) than males in this study. Patients were more likely to be in their 30s (27.2%), be hospitalized for 11-14 days (41.2%), treated with Hwangryunhaedok pharmacopuncture (78.1%), and be prescribed Hwalhyeoljitong decoction (62.3%).The mean Numeric Rating Scale score for patients with knee pain caused by a traffic accident decreased significantly from 4.26 ± 1.39 to 2.53 ± 1.60 (p < 0.001), and the mean Western Ontario and McMaster Universities Osteoarthritis Index score also decreased significantly from 32.72 ± 18.36 to 23.40 ± 15.80 (p < 0.001) following KM treatment.Conclusion: As a result of analyzing 114 hospitalized patients with knee joint pain due to TAs, inpatients were more likely to be female (55%), a patient in their 30s (27.2%), and be a patient hospitalized for 11-14 days (41.2%). KM treatment of traumatic knee injury using pharmacopuncture therapy and herbal medicine can be an may be effective at reducing pain, and healing functional disorders of the knee.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".