Decision Making in the Multiligament‐Injured Knee: An Evidence‐Based Systematic Review
Bibliographic record
Abstract
Purpose The purpose of this systematic review was to address the treatment of multiligament knee injuries, specifically ( 1 ) surgical versus nonoperative treatment, ( 2 ) repair versus reconstruction of injured ligamentous structures, and ( 3 ) early versus late surgery of damaged ligaments. Methods Two independent reviewers performed a search on PubMed from 1966 to August 2007 using the terms “knee dislocation,” “multiple ligament–injured knee,” and “multiligament knee reconstruction.” Study inclusion criteria were ( 1 ) levels I to IV evidence, ( 2 ) “multiligament” defined as disruption of at least 2 of the 4 major knee ligaments, ( 3 ) measures of functional and clinical outcome, and ( 4 ) minimum of 12 months' follow‐up, with a mean of at least 24 months. Results Four studies compared surgical treatment with nonoperative treatment. There was a higher percentage of excellent/good International Knee Documentation Committee (IKDC) scores (58% v 20%) in surgically treated patients, as well as higher rates for return to work (72% v 52%) and return to full sport (29% v 10%). Two studies compared repair with reconstruction of damaged structures, with similar mean Lysholm scores (88 v 87) and excellent/good IKDC scores (51% v 48%). However, repair of the posterolateral corner had a higher failure rate (37% v 9%). Similarly, repair of the cruciates yielded decreased stability and range of motion and a lower return to preinjury activity levels (0% v 33%). There were 5 studies comparing early surgery (≤3 weeks) with late surgery. Early treatment resulted in higher mean Lysholm scores (90 v 82) and a higher percentage of excellent/good IKDC scores (47% v 31%), as well as higher sports activity scores (89 v 69) on the Knee Outcome Survey. Conclusions Our review suggests that early operative treatment of the multiligament‐injured knee yields improved functional and clinical outcomes compared with nonoperative management or delayed surgery. Repair of the posterolateral corner may yield higher revision rates compared with reconstruction.
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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 | 0.026 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".