Optimizing Treatment Strategies and Risk Stratification in Tibial Fractures: A Meta-Analysis of Fixation Timing, Modality, and Fracture Classification on Acute Compartment Syndrome Risk.
Bibliographic record
Abstract
Introduction Acute compartment syndrome (ACS) following tibial fractures can lead to permanent neuromuscular dysfunction and limb amputation if not rapidly diagnosed and treated a correct manner. The risk factors for ACS development in detailed classified manner remain limited, and the management strategies to minimize risk are controversial. We conducted this meta-analysis to quantify ACS risk factors and evaluate the impact of treatment modalities on ACS incidence in tibial fractures. Methods We searched multiple literature scientific databases up to 17 th of April 2025. Primary studies that investigated ACS risk factors in tibial fractures were included. We conducted random and fixed-effects meta-analyses, subgroup analyses, and meta-regression to estimate the risk factors and treatment effects. Study quality was assessed using the Newcastle-Ottawa Scale. Results Seventeen studies with total of 274,962 patients and 10,019 ACS cases met our inclusion criteria. Delayed fixation after 24 hours was associated with 67% reduced ACS risk (OR: 0.33, 95% CI: 0.19-0.58) compared to early fixation, with strongest effects in young males with plateau fractures. Proximal tibial fractures demonstrated significantly higher risk than shaft or distal fractures (OR: 2.02, 95% CI: 1.53-2.66). Male patients had higher risk with two to four folds across age groups. External fixation showed protective effects versus immediate internal fixation, especially for plateau fractures (OR: 0.46, 95% CI: 0.27-0.79). Meta-regression identified fracture type, injury mechanism, and patient demographics explaining 67% of treatment effect variance. Conclusion Our study results are going in an opposite direction to the standard approach of early fixation for tibial fractures, suggesting that delayed fixation or temporary spanning external fixation may significantly reduce ACS risk in high-risk patients. A patient tailored risk-stratified treatment algorithm considering fracture location, patient demographics, and injury mechanism could optimize management according to individual profile to reduce the risk of ACS development furtherly.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.051 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".