Meniscal allograft transplant in a 16-year-old male soccer player: A case report.
Bibliographic record
Abstract
Meniscal allograft transplantation (MAT) is a relatively new procedure that has gained popularity in the last couple of decades as a possible alternative to a meniscectomy to provide significant pain relief, improve function, and prevent the early onset of degenerative joint disease (DJD). As of present, evidence is limited and conflicting on the success of such procedures. In this case, a 16-year old male athlete underwent numerous surgical procedures to correct a left anterior cruciate ligament (ACL) rupture with associated medial and lateral meniscal damage that occurred as a result of a non-contact mechanism of injury. Following multiple procedures, including repair of both menisci and follow-up partial meniscectomy of the lateral meniscus, the patient continued to experience symptoms on the left lateral knee, making him a candidate for MAT. This case is used to highlight what a MAT is, what makes someone a candidate for this type of procedure, the current evidence surrounding the success of this intervention, and some rehabilitation considerations following surgery. The role of chiropractors and primary clinicians is to ensure that young athletes undergo early intervention to offset any degenerative changes that would be associated with sustained meniscal lesions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".