The Functional Outcomes of Delayed Surgical Reconstruction in Nonsport-Induced Multiligament Knee Injuries: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Multiligament knee injuries (MLKIs) are among the most detrimental injuries, which can cause significant compromise of joint stability and function. Our aim was to investigate the functional outcomes of nonsport-induced MLKIs who presented late after injury and underwent delayed arthroscopic reconstruction. In a retrospective cohort of 18 MLKI patients (19 knees, January 2012–2018) who had undergone arthroscopic reconstruction, we assessed the knee range of motion, return to work/sport, International Knee Documentation Committee (IKDC), Knee Injury and Osteoarthritis Outcome Score, Western Ontario and McMaster Universities Arthritis Index, Lysholm, and Tegner scores. The preoperative scores were retrieved from the patients' registry database. We reviewed their surgical notes and extracted the operation data, including the damaged ligaments, stages of the surgery, and associated meniscal injury. There were 14 males and 4 females with a mean age of 30.57 ± 10.31 years. The mean time from injury to surgery was 17.31 ± 11.98 months. The most common injury was anterior cruciate ligament/posterior cruciate ligament (31.6%). The mechanisms of injury were motor vehicle accidents (72.2%), falls (22.2%), and sports (5.6%). The reconstruction was either single (61.2%) or multiple stage (38.8%). The pre- and postoperative scores were 45.31 ± 7.30 versus 79.16 ± 11.86 IKDC, 3.84 ± 1.26 versus 8.37 ± 1.16 Tegner, and 60.42 ± 7.68 versus 89.42 ± 8.81 Lysholm, respectively. All the scores showed significant improvement at mean follow-up of 24.05 ± 9.55 months (p < 0.001). In conclusion, delayed arthroscopic reconstruction of MLKIs significantly improved the functional outcomes and return to work in patients presenting late to the orthopaedic clinic. There was no relationship between the demographic variables, mechanism of injury, number of injured ligaments, and the stages of surgery and the functional outcomes in this group of patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".