Evidence-Based Validation of the Predictive Value of the American Association for the Surgery of Trauma Kidney Injury Scale
Bibliographic record
Abstract
BACKGROUND: To evaluate the predictive value of the American Association for the Surgery of Trauma (AAST) kidney injury scale for the management of traumatic renal injuries. METHODS: From October 1995 through October 2004, 424 patients presented to our hospital with traumatic renal injury. RESULTS: Overall, 27.8% of patients had grade I injury, 26.4% had grade II injury, 19.3% had grade III injury, 18.2% had grade IV injury, and 8.3% had grade V injury. Patient age, Glasgow Coma Scale score, Revised Trauma Score, creatinine, blood urea nitrogen (BUN), white blood count, gender, substance abuse, shock, flank ecchymosis, abdominal pain, and mortality were not associated with AAST grade. Systolic blood pressure and hematocrit levels decreased with increasing AAST grades (p = 0.032 and p = 0.045, respectively). Volume transfused and length of hospitalization increased with AAST grades (p = 0.003 and p = 0.004, respectively). Patients with gunshot injury had higher AAST grades than those with blunt trauma (p < 0.001). Hypotension (14%), blood transfusion (47%), gross hematuria (65.9%), and flank pain (25%) were associated with higher AAST grades (p = 0.010, p < 0.001, p = 0.016, and p = 0.001, respectively). Ninety patients (21.2%) underwent renal exploration: 61% nephrectomies and 39% renorraphies. In multivariable analyses, type of injury, hematuria at presentation, and AAST scale predicted the risk of renal exploration (p < 0.001, p = 0.024, and p < 0.001, respectively), whereas type of injury and AAST scale were the sole predictors of nephrectomy (p < 0.001 and p < 0.001, respectively). CONCLUSIONS: We confirmed that the AAST injury severity scale is a powerful and valid tool for prediction of clinical outcome in patients with 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 | 0.048 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".