The Use of Synthetic Graft for MPFL Reconstruction Surgery: A Systematic Review of the Clinical Outcomes
Bibliographic record
Abstract
(1) Background: Acute patella dislocation (APD) is a prevalent knee injury, with rates between 5.8-77.8 per 100,000. APD often results in repeat lateral patella dislocations due to the instability of the medial patellofemoral ligament (MPFL). Non-operative treatments have a 50% recurrence rate. While autologous grafting for MPFL has been favored, surgeons are now exploring synthetic grafts. We aimed to assess the effectiveness of synthetic grafts in MPFL reconstruction surgeries for repeated patellar dislocations; (2) Methods: Our research was based on a thorough search from the National Institute of Health and Clinical Excellence Healthcare Databases, using the Modified Coleman Methodology Score for quality assessment; (3) Results: Six studies met the inclusion criteria. A total of 284 patients and 230 knees were included. Seventy-five percent of patients were graded to have excellent-good clinical outcomes using the Crosby and Insall Grading System. International Knee Documentation Committee score and Knee injury and Osteoarthritis Outcome Score scores showed 59% and 60% post-operative improvement, respectively; (4) Conclusions: All studies showed improvement in post-operative functional outcomes and report no serious adverse events. The 6 mm, LARS (Orthomedic Ltd., Dollard-des-Ormeaux, QC, Canada) proved to have the most improvement in post-operative outcomes when used as a double bundle graft.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".