Nationwide Procedural Trends for Renal Trauma Management
Bibliographic record
Abstract
OBJECTIVE: To characterize national trends in procedural management of renal trauma. BACKGROUND: Management of renal trauma has evolved to favor a more conservative approach. For patients requiring intervention, there is a paucity of information to characterize the nature of procedural therapy administered. METHODS: A retrospective cross-sectional analysis was performed using data contained within the National Trauma Data Bank. The National Trauma Data Bank is a voluntary data repository managed by the American College of Surgeons, containing data regarding trauma admissions at 747 level I to V trauma centers throughout the United States and Canada. Participants included any patient with renal trauma requiring intervention from 2002 to 2012. They were identified according to International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes, with codes 866.00 through 866.03 for blunt renal trauma, and codes 866.10 through 866.13 for penetrating trauma. Cases were separated into those requiring nephrectomy, renorrhaphy, or endovascular repair based on ICD-9 procedure code. The number of cases performed each year and yearly trends as measured by linear regression. RESULTS: A total of 4296 cases were reported during the study period. Of these cases, 2635 involved blunt trauma and 1661 involved penetrating injury. There was a significant increase in the percentage of cases managed by endovascular means for both blunt and penetrating trauma (R = 0.92, P < 0.01; and R = 0.86, P < 0.01, respectively). This was primarily at the expense of nephrectomy, with cases showing significant decline in both groups. CONCLUSIONS: National trends for procedural management of renal trauma are toward less invasive interventions. These trends suggest favorable change towards renal preservation and decreased morbidity, potentially facilitated, in part, by improved radiographic staging and endovascular techniques, and also increased provider awareness of the safety and value of conservative 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".