Pie‐crusting the medial collateral ligament is a safe and effective technique for improving visualisation and access in arthroscopic meniscal surgery: A systematic review
Bibliographic record
Abstract
PURPOSE: To assess the safety, effectiveness, and postoperative outcomes of medial collateral ligament (MCL) pie-crusting (PC) in arthroscopic meniscus surgery. METHODS: This systematic review was conducted in accordance with PRISMA guidelines. Three databases (PubMed, EMBASE and MEDLINE) were searched from inception to 21 January 2025, for studies evaluating the use of MCL PC during arthroscopic meniscus or anterior cruciate ligament (ACL) surgery. Data on patient demographics, medial joint space measurements, postoperative instability, patient reported outcome measures (PROMs), and complications were extracted. RESULTS: Fifteen studies comprising 1009 patients were included, with 723 undergoing PC. PC significantly increased medial joint space width by a mean of 5.7 mm intraoperatively (p < 0.05), with no residual laxity reported at mean final follow-up of 16.5 months. The most common complications of PC were transient medial knee pain (12.7%) and ecchymosis (12.9%). There were five reports of saphenous nerve irritation (0.8%), all resolved by final follow-up of 31.6 months. In contrast, iatrogenic chondral injury was reported in 9.0% of patients undergoing meniscal repair without PC. CONCLUSION: MCL PC is a safe and effective technique to improve visualisation and outcomes in arthroscopic meniscus surgery. It is associated with improved joint access and low complication rates without long-term instability. While current evidence supports its use, further high-quality, long-term comparative studies are needed to validate its safety and efficacy, as this study primarily used retrospective data without long-term follow-up. LEVEL OF EVIDENCE: Level IV.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".