The use of a multi-modal approach in the rehabilitation of a pre-operative grade 3 ACL tear in a world-level Poomsae athlete: a case report.
Bibliographic record
Abstract
OBJECTIVE: This case is designed to aid practitioners in understanding the potential role of multi-modal care with vibration rehabilitative exercise for a complete ACL tear in a high-level Poomsae athlete. CASE PRESENTATION: A 16-year-old male world-class Poomsae athlete presented with a right ACL rupture and LCL sprain. An extensive clinical examination and imaging confirmed a right grade 3 ACL tear. Due to the complete tear and impending participation in World Championships, a pre-operative rehabilitation strategy was implemented with treatment modalities aimed to accelerate return-to-play. SUMMARY: An appropriate clinical history and physical examination of the knee is required when instability is present. Imaging is indicated when testing criteria are positive. Clinicians should be aware that multiple therapies can each serve a role in conservative care to better suit patient demands, especially at high levels of sport. In the article, the author proposes a tailored protocol using vibration rehabilitative exercise, bracing, vibration therapy, neuromuscular electrical stimulation, and laser to improve healing and sport-specific outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".