High Incidence of Soft Tissue Injury in Pediatric Proximal Tibia Fractures: A Systematic Review
Bibliographic record
Abstract
Purpose: The purpose of this study was to describe the incidence of soft tissue injuries associated with pediatric proximal tibial fractures (PPTF) and the frequency that magnetic resonance imaging (MRI) was used before surgery in this patient population. Methods: A systematic review of English literature, using EMBASE and PubMed, was completed. Articles reporting on soft tissue injury in PPTFs between 1980 and 2021 were identified. Associated pathology (meniscal tear, meniscal entrapment, cruciate ligament injury, extensor mechanism injury, and chondral injury) and use of MRI at time of diagnosis, were assessed in these studies. Twenty-three articles were included. Results: Extraction of data revealed 1046 patients and 1057 fractures, with a mean age of 12.3 ± 1.7 at the time of injury. Most patients were male (n = 757 [72.3%]). Most fractures were tibial eminence fractures (TEF) (n = 747 [70.7%]), followed by tibial tubercle (n = 218 [20.6%]) and then tibial plateau fractures (n = 92 [8.7%]). Associated soft tissue injuries were found in 58.8% (n = 621) of fractures overall. Meniscal entrapment was the most common, occurring in 22.1% (n = 234) of cases. Meniscal tears occurred in 18.6% of cases (n = 197), followed by ligament injury in 9.4% (n = 99), chondral injury in 6.5% (n = 69), and extensor mechanism injury in 2.1% (n = 22) of cases. All cases of tendinous extensor mechanism injury were seen in tibial tubercle fractures, with 22 injuries occurring in 10.1% of tibial tubercle fractures. At time of injury just 24.3% (n = 257) of fractures had an MRI performed before surgery. Conclusions: PPTFs are associated with a high incidence of associated injury (58.8%), particularly in TEFs (63.5%) and TPFs (100%). Level of Evidence: Systematic Review of Level III-IV studies.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".