Splenic artery embolisation in the non-operative management of blunt splenic trauma in adults
Bibliographic record
Abstract
Background: The purpose of this study was to evaluate the splenic salvage rate with angioembolisation in the non-operative management (NOM) of blunt splenic injury.Methods: We conducted a retrospective analysis of patients presenting to our Level I trauma centre with computed tomography (CT)-confirmed splenic injury following blunt trauma and in whom angioembolisation was utilised in the algorithm of NOM. Data review included CT and angiography findings, embolisation technique and patient outcomes.Results: Between January 2005 and April 2010, 60 patients with splenic injury following blunt trauma underwent NOM, which included splenic artery embolisation (SAE). All patients included in the study required a preadmission. CT scan was used to document the American Association for the Surgery of Trauma (AAST) grade of splenic injury. The average injury grade was 3.0. The non-operative splenic salvage rate following SAE was 96.7% with statistically similar salvage rates achieved for grades II to IV injuries. The quantity of haemoperitoneum and the presence of a splenic vascular injury did not significantly affect the splenic salvage rate. The overall complication rate was 27%, of which 15% were minor and 13% were major.Conclusion: SAE is a safe and effective treatment strategy in the NOM of blunt splenic injury. The quantity of haemoperitoneum, the presence of vascular injury and embolisation technique did not significantly affect the splenic salvage rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".