Outcomes of Operative and Nonoperative Treatment of Multiligament Knee Injuries
Bibliographic record
Abstract
Knee dislocation is an uncommon orthopedic diagnosis with a high rate of neurovascular complications. The goal of definitive management is to provide a pain free and functional knee through restoration of ligamentous stability and range of motion. Operative management has been suggested to be superior to nonoperative management for knee dislocations largely in part owing to a meta-analysis on the topic published a decade ago. The purpose of this study was to summarize the results of operative and nonoperative treatment of knee dislocations over the past 10 year period. There were a total of 855 patients from 31 studies managed operatively and 61 patients from 4 studies managed nonoperatively. The overall methodological quality of the studies was poor as measured by the Newstead-Ottawa scale. Data regarding functional outcome, instability, contracture, and return to activity were all in favor of operative management. Significant differences were found for return to employment (P<0.001) and return to sport (P=0.001). The results of this study provide further evidence for the superiority of operative management, compared with nonoperative management, for knee dislocations across several clinical and functional domains. There is a need for higher level studies to assist the treating surgeon in the management of these challenging injuries.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".