Clinical Predictors of Intra‐Abdominal Injury in Severe Blunt Trauma Patient
Bibliographic record
Abstract
Introduction The early recognition of intra‐abdominal injury (IAI) in patients with blunt trauma is essential, yet physical examination is often unreliable. Computed tomography (CT) scans are used widely to further evaluate possible IAI but these require time and expense. IAI may be associated with certain objective risk factors or other specific injuries, but this association has not been widely studied. Identification of such risk factors will help to prioritize patients in need for further evaluation of possible IAI. Methods A retrospective chart review was conducted of all 622 adult severe blunt trauma patients (Injury Severity Score [ISS] >12) presenting to a level 1 trauma centre in 2004. Various clinical predictors of IAI were analyzed statistically with univariate and multivariate analysis using SAS software. Results In multivariate analysis, four significant predictors of IAI were found: positive focused assessment with sonography for trauma (FAST) (OR=48.5, p<0.0001), presence of pelvic fracture (OR=2.4, p=0.0002), chest tube insertion (OR=1.8, p=0.0211), and systolic blood pressure (SBP), where every 10 mmHg decrease indicates a 14% increase in risk (OR=0.986, p=0.001). The absence of all four predictors predicted the absence of IAI with a specificity of 0.776 (95% CI 0.741 to 0.808) and a LR of 2.7 (95% CI 2.0 to 3.5). Conclusion This study suggests that positive FAST, presence of pelvic fracture, chest tube insertion, and SBP are significant predictors of IAI in adult blunt trauma patients with ISS > 12. The absence of all four predictors is associated with a reduced risk of IAI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".