Retrospective Evaluation of Splenic Artery Embolization Outcomes in the Management of Blunt Splenic Trauma: A Single Centre Experience at a Large Level 1 Trauma Centre
Bibliographic record
Abstract
PurposeRetrospective review of splenic artery embolization (SAE) outcomes performed for blunt abdominal trauma.Materials and Methods11-year retrospective review at a large level-1 Canadian trauma centre. All patients who underwent SAE after blunt trauma were included. Technical success was defined as angiographic occlusion of the target vessel and clinical success was defined as successful non-operative management and splenic salvage on follow-up.Results138 patients were included of which 68.1% were male. The median age was 47 years (interquartile range (IQR) = 32.5 years). The most common mechanisms of injury were motor vehicle accidents (37.0%), mechanical falls (25.4%), and pedestrians hit by motor vehicles (10.9%). 70.3% of patients had American Association for the Surgery of Trauma (AAST) grade 4 injuries. Patients were treated with proximal SAE (n = 97), distal SAE (n = 23) or combined SAE (n = 18), and 68% were embolized with an Amplatzer plug. No significant differences were observed across all measures of hospitalization (Length of hospital stay: x2(2) = .358, P = .836; intensive care unit (ICU) stay: x2(2) = .390, P = .823; ICU stay post-procedure: x2(2) = 1.048, P = .592). Technical success and splenic salvage were achieved in 100% and 97.8% of patients, respectively. 7 patients (5%) had post-embolization complications and 7 patients (5%) died during hospital admission, but death was secondary to other injuries sustained in the trauma rather than complications related to splenic injury or its management.ConclusionWe report that SAE as an adjunct to non-operative management of blunt splenic trauma can be performed safely and effectively with a high rate of clinical success.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".