The role of angioembolization in the management of blunt renal injuries: a systematic review
Bibliographic record
Abstract
BACKGROUND: Recently, renal angioembolization (RAE) has gained an important role in the non-operative management (NOM) of moderate to high-grade blunt renal injuries (BRI), but its use remains heterogeneous. The aim of this review is to examine the current literature on indications and outcomes of angioembolization in BRI. METHODS: We conducted a search of MEDLINE, EMBASE, SCOPUS and Web of Science Databases up to February 2021 in accordance with PRISMA guidelines for studies on BRI treated with RAE. The methodological quality of eligible studies and their risk of bias was assessed using the Newcastle-Ottawa scale RESULTS: A total of 16 articles that investigated angioembolization of blunt renal injury were included in the study. Overall, 412 patients were included: 8 presented with grade II renal trauma (2%), 97 with grade III renal trauma (23%); 225 with grade IV (55%); and 82 with grade V (20%). RAE was successful in 92% of grade III-IV (294/322) and 76% of grade V (63/82). Regarding haemodynamic status, success rate was achieved in 90% (312/346) of stable patients, but only in 63% (42/66) of unstable patients. The most common indication for RAE was active contrast extravasation in hemodynamic stable patients with grade III or IV BRI. CONCLUSIONS: This is the first review assessing outcomes and indication of angioembolization in blunt renal injuries. The results suggest that outcomes are excellent in hemodynamic stable, moderate to high-grade renal trauma.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".