Knee dislocation and associated injuries: an analysis of the American College of Surgeons National Trauma Data Bank
Bibliographic record
Abstract
PURPOSE: Knee dislocations (KDs) are potentially devastating injuries, leading to loss of function or limb in often young patients. This retrospective database review aims to determine the relative incidence and risk factors for KDs presenting to North American Level I and II trauma centers. METHODS: The National Trauma Data Bank (NTDB) was retrospectively interrogated using ICD-9-CM codes to identify KDs between 2010 and 2014 to derive KD incidence. KDs were stratified by age, sex, Injury Severity Score (ISS), Glasgow Coma Scale (GCS), drug and alcohol use, injury mechanism, open vs. closed KD, vascular injury and fracture. Each co-variate was tested against different mechanisms of injury, using Chi-squared tests and risk adjusted analyses to derive risk factors for KD. The same calculations were done for secondary outcomes (vascular and neurological injuries, compartment syndrome, amputation, and mortality). RESULTS: A total of 6454 KDs met the inclusion criteria (18/10,000 admissions). KDs occurred most commonly amongst men, aged 20-39, with an ISS score 1-14 and following motor vehicle collision (MVC). A vascular investigation was performed in 29%, with injury documented in 15% of KDs and 10.8% receiving a vascular procedure. Associated fractures were observed in 41.4% of KDs. Open injuries in 13.6%. Neurological injury documented in 6.2%, compartment syndrome in 2.7%, amputation in 3.8% (> 50% had vascular injury) and 2.8% died. MVC was the most common mechanism of injury (p < 0.001), significantly more common in young, male patients, associated with higher ISS and lower GCS, especially when drugs or alcohol were involved (p < 0.0001). Being male, having a vascular injury or open KD were all risk factors for compartment syndrome, amputation and neurological injuries. CONCLUSIONS: KDs are rare injuries, but their relative incidence may be increasing. Young, male patients involved in MVCs are risk factors for KDs and their associated injuries, such as neurological injuries, amputations and compartment syndrome. Vascular injury occurs at a frequency of around 15%. The findings of the current study may guide future research and help to inform clinicians on the expected rates of associated injuries in patients identified to have KD in a trauma center population. It informs regarding risk factors for KD, which may improve diagnosis rates of spontaneously reduced knee dislocations by increasing index of suspicion in high-risk patients and identifies specific links with impaired driving. LEVEL OF EVIDENCE: IV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".