Management of high-grade renal traumas with collecting system injuries
Bibliographic record
Abstract
INTRODUCTION: Approximately 50% of all high-grade renal traumas (HGRT, American Association for the Surgery of Trauma [AAST] grade 4/5) have associated collecting system injuries. Although most of these collecting system injuries will heal spontaneously, approximately 20-30% of these injuries are managed with ureteric stents. The objective of the study was to review the management of HGRT with collecting system injuries in a level 1 trauma center. METHODS: This was a single-center, retrospective cohort study of trauma patients with HGRT and collecting system injuries from 1998-2019. RESULTS: We identified 147 patients with HGRT. Of the 105 patients who had trauma computed tomography (CT) imaging within 24 hours, 46 were found to have collecting system injuries. Seven of these patients underwent intervention based on initial CT findings; the remaining 39 patients with urinary extravasation were conservatively managed. Of the 37 patients who underwent reimaging, 22 (59%) demonstrated a stable or resolving collection and 15 (41%) demonstrated continued urinary extravasation. Resolution of extravasation on subsequent imaging was observed in 10 of those patients, while five patients (14%) required intervention (four stents, one percutaneous drain) for symptoms/signs of urinary extravasation. CONCLUSIONS: In this study, most patients with HGRT and collecting system injuries did not require intervention unless the patient became symptomatic. The majority of collecting system injuries resolved with no intervention. This study underscores the need for future prospective trials to investigate the necessity of intervening in HGRT collecting system injuries and, secondarily, the need for routine re-imaging in these asymptomatic patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".