Perinephric Hematoma Size is Independently Associated with the Need for Urological Intervention in Multisystem Blunt Renal Trauma
Bibliographic record
Abstract
PURPOSE: We examined radiographic predictors of intervention for blunt renal trauma independent of AAST-OIS (American Association for the Surgery of Trauma-Organ Injury Scale). MATERIALS AND METHODS: A total of 328 patients with blunt renal trauma from October 2004 to December 2014 were identified for analysis. Hospital records and diagnostic imaging were reviewed to identify the need for urological intervention, including angiographic embolization, nephrectomy, renorrhaphy, ureteral stenting or percutaneous drainage. Factors examined included patient age, gender, length of stay, ISS (Injury Severity Score), AAST-OIS, laceration location, length and number, perinephric hematoma characteristics, intravascular contrast extravasation and devitalized segment status. Descriptive statistics and binary logistic regression were performed as appropriate. RESULTS: Mean patient age was 37.0 years and mean ISS was 31.7. A total of 31 urological interventions were required in 27 patients (8.2%), including ureteral stenting in 38.7%, angiographic embolization in 32.3%, nephrectomy in 22.6%, renorrhaphy in 3.2% and percutaneous drainage in 3.2%. On univariate analysis AAST-OIS, hematoma diameter, hematoma area, intravascular contrast extravasation, laceration length, laceration number, degree of devitalization and devitalized fragment presence were associated with the need for intervention (each p <0.001). On multivariate analysis only AAST-OIS grade (OR 69.4, 95% CI 6.4-748.3, p <0.001) and hematoma diameter (OR 1.5, 95% CI 1.1-1.9, p = 0.004) or area (OR 1.03, 95% CI 1.01-1.06, p = 0.012) remained associated with urological intervention. CONCLUSIONS: Although AAST-OIS is strongly associated with the need for urological intervention, perinephric hematoma size is also independently associated with this occurrence. Perinephric hematoma diameter should be considered during clinical decision making and incorporated into a revised injury grading system.
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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 | 0.001 |
| 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) | 0.002 | 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".