Management of Iatrogenic Medial Collateral Ligament Injury in Primary Total Knee Arthroplasty: A Systematic Review.
Bibliographic record
Abstract
Objectives: The medial collateral ligament (MCL) injury is one of the possible complications of primary total knee arthroplasty (TKA), which can lead to coronal-plane instability that requires surgical revision. Injured MCL can result in joint instability and polyethylene wear. Different strategies have been proposed for MCL reconstruction based on the location of the injury. However, there is a lack of clarity regarding the optimal method for handling an iatrogenic MCL injury throughout a TKA. Methods: A PRISMA flow diagram was used to guide the systematic literature review. An extensive search was conducted in PubMed, Embase, Scopus, Web of Science, and Google Scholar. Newcastle Ottawa scale checklist was used to assess the methodological quality of the articles. Results: A total of 19 qualitative studies, including non-cadaveric patients with MCL injury during TKA, were identified after analyzing the full text of the articles. All included studies were either retrospective, observational cohort or case series. A total of 486 patients were studied to gather information on the methods used to repair the MCL and their results. Most injuries arose in the tibial attachment, which surgeons mostly realized during the final stages of surgery. Used techniques can be categorized into three main groups: Primary repair, Repair with augmentation, and changing prosthesis characteristics. Conclusion: This systematic review demonstrated that the most popular management of iatrogenic MCL injury was using suture anchors, staples, screws and washers, and more constrained prostheses. The proper method should be decided considering the site of the MCL injury.
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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) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".