Diagnosis and Treatment Strategies of Meniscus Root Tears: A Scoping Review
Bibliographic record
Abstract
Background: Meniscus root tears comprise 10% to 21% of all meniscal tears. These tears alter knee biomechanics, elevating contact pressure, akin to a meniscectomy. Consequently, they are linked to advanced joint degeneration and cartilage damage in the affected compartment. Purpose: To systematically evaluate and relate the current literature describing the diagnosis and treatment strategies for meniscus root tears. Study Design: Scoping review; Level of evidence, 4. Methods: This review was conducted following the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Inclusion criteria encompassed English-language clinical and preclinical research, technical notes, and narrative reviews on meniscus root tears. Exclusion criteria were studies on patellar tendon rupture, studies on medial patellofemoral ligament rupture with additional knee joint ligament injuries, studies of patients <16 years old, and studies involving open fractures. The data were summarized using a descriptive analysis and a thematic analysis. Results: After 1425 articles were identified, 461 studies were included; 17% (n = 78) were case reports or case series, 15% (n = 71) were technical notes, 9% (n = 41) addressed aspects of diagnosis, 7% (n = 32 ) were narrative reviews, and 5% (n = 21) were systematic reviews or meta-analyses. Studies presenting original data comprised 57% (n = 262) of all included studies, and 97% of the studies were of evidence levels 3 to 5. Contributions were mainly from the United States (n = 123; 27%), Republic of Korea (n = 102; 22%), and Japan (n = 99; 21%). Many studies (n = 216; 47%) focused on the treatment and outcomes of meniscus root tears. A significant chronological surge in the quantity of studies addressing the diagnosis and treatment of meniscus root tears was evident, particularly over the past 3 years. Consensus was found regarding the definition of meniscus root tears, the advantages of early repair, and postoperative rehabilitation protocols. The variations in surgical techniques and operative strategies created the greatest amount of contention, along with clinical assessment and imaging modalities. Conclusion: High-level evidence studies for diagnosing and managing meniscus root tears were scarce. A consensus has yet to be reached regarding the role of concomitant osteotomy, comparison of repair techniques, the use of a centralization stitch, patient factors affecting outcomes, and long-term outcomes of nonoperative management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".