Accuracy of single-pass whole-body computed tomography for detection of injuries in patients with major blunt trauma
Bibliographic record
Abstract
BACKGROUND: Contrast-enhanced whole-body computed tomography (also called "pan-scanning") is considered to be a conclusive diagnostic tool for major trauma. We sought to determine the accuracy of this method, focusing on the reliability of negative results. METHODS: Between July 2006 and December 2008, a total of 982 patients with suspected severe injuries underwent single-pass pan-scanning at a metropolitan trauma centre. The findings of the scan were independently evaluated by two reviewers who analyzed the injuries to five body regions and compared the results to a synopsis of hospital charts, subsequent imaging and interventional procedures. We calculated the sensitivity and specificity of the pan-scan for each body region, and we assessed the residual risk of missed injuries that required surgery or critical care. RESULTS: A total of 1756 injuries were detected in the 982 patients scanned. Of these, 360 patients had an Injury Severity Score greater than 15. The median length of follow-up was 39 (interquartile range 7-490) days, and 474 patients underwent a definitive reference test. The sensitivity of the initial pan-scan was 84.6% for head and neck injuries, 79.6% for facial injuries, 86.7% for thoracic injuries, 85.7% for abdominal injuries and 86.2% for pelvic injuries. Specificity was 98.9% for head and neck injuries, 99.1% for facial injuries, 98.9% for thoracic injuries, 97.5% for abdominal injuries and 99.8% for pelvic injuries. In total, 62 patients had 70 missed injuries, indicating a residual risk of 6.3% (95% confidence interval 4.9%-8.0%). INTERPRETATION: We found that the positive results of trauma pan-scans are conclusive but negative results require subsequent confirmation. The pan-scan algorithms reduce, but do not eliminate, the risk of missed injuries, and they should not replace close monitoring and clinical follow-up of patients with major 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".