Risk factors for acute compartment syndrome of the leg associated with tibial diaphyseal fractures in adults
Bibliographic record
Abstract
BACKGROUND: We sought to examine the occurrence of acute compartment syndrome (ACS) in the cohort of patients with tibial diaphyseal fractures and to detect associated risk factors that could predict this occurrence. MATERIALS AND METHODS: A total of 1,125 patients with tibial diaphyseal fractures that were treated in our centre were included into this retrospective cohort study. All patients were treated with surgical fixation. Among them some were complicated by ACS of the leg. Age, gender, year and mechanism of injury, injury severity score (ISS), fracture characteristics and classifications and the type of fixation, as well as ACS characteristics in affected patients were studied. RESULTS: Of the cohort of patients 772 (69 %) were male (mean age 39.60 ± 15.97 years) and the rest were women (mean age 45.08 ± 19.04 years). ACS of the leg occurred in 87 (7.73 %) of all tibial diaphyseal fractures. The mean age of those patients that developed ACS (33.08 ± 12.8) was significantly lower than those who did not develop it (42.01 ± 17.3, P < 0.001). No significant difference in incidence of ACS was found in open versus closed fractures, between anatomic sites and following IM nailing (P = 0.67). Increasing pain was the most common symptom in 71 % of cases with ACS. CONCLUSIONS: We found that younger patients are definitely at a significantly higher risk of ACS following acute tibial diaphyseal fractures. Male gender, open fracture and IM nailing were not risk factors for ACS of the leg associated with tibial diaphyseal fractures in adults. LEVEL OF EVIDENCE: Level IV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".