Imaging of Traumatic Diaphragmatic Rupture: Evaluation of Diagnostic Accuracy at a Level 1 Trauma Centre
Bibliographic record
Abstract
PURPOSE: Traumatic diaphragmatic rupture (TDR) is an uncommon injury that can be associated with significant morbidity if not detected and treated in a timely manner. The purpose of our study was to evaluate the diagnostic accuracy of 64-slice multidetector computed tomography (64-MDCT) for the detection of TDR in patients at our level 1 trauma centre. METHODS: We used our hospital's trauma registry to identify patients with a diagnosis of TDR from January 1, 2008, to December 31, 2012. Only patients with a 64-MDCT scan at presentation who subsequently underwent laparotomy/laparoscopy were included in the study cohort. Using surgical findings as the gold standard, the accuracy of the prospective radiology reports was analyzed. RESULTS: Of the 3225 trauma patients who presented to our institution, 38 (1.2%) had a TDR. Fourteen of the 38 were excluded as they did not have MDCT before surgery. The study cohort consisted of 20 males and 4 females with a median age of 34.5 years and a median Injury Severity Score (ISS90) of 26. Fifteen had blunt trauma while 9 had a penetrating injury. The overall sensitivity of the radiology reports was 66.7% (95% confidence interval [CI]: 46.7%-82.0%), specificity was 100% (95% CI: 94.1%-100%), positive predictive value was 100% (95% CI: 80.6%-100%), negative predictive value was 88.4% (95% CI: 78.8%-94.0%), and accuracy was 90.6% (95% CI: 82.5%-95.2%). However, only 3 of 9 patients with penetrating injury had a correct preoperative diagnosis. Two of the 6 missed penetrating trauma cases had only indirect signs of injury. CONCLUSIONS: The detection of TDR in trauma patients on 64-MDCT can be improved, especially in patients presenting with penetrating injury. A careful search for subtle diaphragmatic defects and indirect evidence of injury is important to avoid missing the diagnosis.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".