Sacral Fractures and Associated Injuries
Bibliographic record
Abstract
STUDY DESIGN: Literature review. OBJECTIVE: The aim of this review is to describe the injuries associated with sacral fractures and to analyze their impact on patient outcome. METHODS: A comprehensive narrative review of the literature was performed to identify the injuries associated with sacral fractures. RESULTS: Sacral fractures are uncommon injuries that result from high-energy trauma, and that, due to their rarity, are frequently underdiagnosed and mistreated. Only 5% of sacral fractures occur in isolation. Injuries most often associated with sacral fractures include neurologic injuries (present in up to 50% of sacral fractures), pelvic ring disruptions, hip and lumbar spine fractures, active pelvic/ abdominal bleeding and the presence of an open fracture or significant soft tissue injury. Diagnosis of pelvic ring fractures and fractures extending to the lumbar spine are key factors for the appropriate management of sacral fractures. Importantly, associated systemic (cranial, thoracic, and abdominopelvic) or musculoskeletal injuries should be promptly assessed and addressed. These associated injuries often dictate the management and eventual outcome of sacral fractures and, therefore, any treatment algorithm should take them into consideration. CONCLUSIONS: Sacral fractures are complex in nature and often associated with other often-missed injuries. This review summarizes the most relevant associated injuries in sacral fractures and discusses on their appropriate management.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".