Effect of Postoperative Rehabilitation Using Muscle Energy Technique in a Patient with Complete Patellar Tendon Rupture Accompanied by Patellar Avulsion Fracture: A Case Report
Bibliographic record
Abstract
Objectives This study reports the clinical outcomes of integrative Korean medicine treatment in a patient who underwent surgical repair for patellar tendon rupture with patellar avulsion fracture. Methods A 27-year-old male received integrative Korean medicine treatment six weeks after surgery, including acupuncture, pharmacopuncture, Chuna manual ther-apy (rectus femoris muscle energy technique), and exercise instruction. Clinical out-comes were assessed using the Numeric Rating Scale (NRS), Range of Motion (ROM), Western Ontario and McMaster Universities Arthritis Index (WOMAC), and Short Form-36 Health Survey (SF-36). Results Pain decreased from 5 to 2 on the NRS, WOMAC improved from 59 to 11, and knee flexion increased from 60°/80° to 130°/150°. The SF-36 score improved from 50.1 to 80.0. Despite postoperative stiffness from six weeks of brace immobili-zation, the patient achieved rapid functional recovery and daily activity resumption. Conclusions Integrative Korean medicine may aid pain reduction, knee mobility re-storation, and quality-of-life improvement after patellar tendon repair.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".