Single-Stage vs. Multi-Stage Reconstruction in Multi-Ligament Knee Injuries: A Systematic Review and Meta-Analysis of Outcomes and Complications
Bibliographic record
Abstract
Background/objectives: Multi-ligament knee injuries (MLKIs) present complex surgical challenges, and there remains no consensus on whether single-stage or staged reconstruction yields superior outcomes. This study aimed to assess differences in complications, functional outcomes, and return-to-sport rates between single-stage and staged surgical approaches. Materials and Methods: A systematic review was conducted in accordance with PRISMA guidelines. Four databases (PubMed, Scopus, Embase, and the Cochrane Library) were searched for studies published between 2000 and 2025. Eligible studies reported surgical management of MLKIs and specified either single-stage or multi-stage reconstruction. Data on complications, functional scores (Lysholm), return to sport, rehabilitation protocols, and graft type were extracted and analyzed using descriptive statistics and study-level regression models. Results: A total of 43 studies encompassing 2086 patients were included (1900 single-stage; 186 multi-stage). Staged reconstruction was associated with a significantly lower rate of arthrofibrosis (1.95% vs. 7.29%; OR 3.96, p = 0.007), higher Lysholm scores (+4.7 points, p < 0.001), and higher return-to-sport rates (48% vs. 65%, p = 0.001) compared to single-stage. Use of synthetic grafts increased the risk of arthrofibrosis (OR 4.09, p = 0.031). Early mobilization and weightbearing were not associated with increased arthrofibrosis risk. Conclusions: Staged reconstruction may yield better functional outcomes and lower complication rates—particularly arthrofibrosis, compared to single-stage approaches. These findings support an individualized surgical strategy, guided by injury complexity, graft selection, rehabilitation goals, and patient-specific functional demands.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".