Use of abdominal computed tomography in blunt trauma: do we scan too much?
Bibliographic record
Abstract
OBJECTIVES: To determine what proportion of abdominal computed tomography (CT) scans ordered after blunt trauma are positive and the applicability and accuracy of existing clinical prediction rules for obtaining a CT scan of the abdomen in this setting. SETTING: A leading trauma hospital, affiliated with the University of Ottawa. DESIGN: A retrospective cohort study. PATIENTS AND METHODS: All patients with blunt trauma admitted to hospital over a 1-year period having an Injury Severity Score (ISS) greater than 12 who underwent CT of the abdomen during the initial assessment. Recorded data included age, sex, Glasgow Coma Scale (GCS) score, ISS, type of injuries, number of abdominal CT scans ordered, and scan results. Two clinical prediction rules were found in the literature that identify patients likely to have intra-abdominal injuries. These rules were applied retrospectively to the cohort. The predicted proportion of positive CT scans was compared with the observed proportion, and the sensitivity, specificity, and accuracy were estimated. RESULTS: Of the 297 patients entered in the study, 109 underwent abdominal CT. The median age was 32 years, 71% were male and the median ISS was 24. In only 36.7% (40 of 109) of scans were findings suggestive of intra-abdominal injuries. Application of one of the clinical prediction rules gave a sensitivity of 93.8% and specificity of 25.5% but excluded 23% of patients because of a GCS score less than 11. The second prediction rule tested could be applied to all patients and was highly sensitive (92.5%) and specific (100.0%). CONCLUSIONS: The assessment of the abdomen in blunt trauma remains a challenge. Accuracy in predicting positive scans in equivocal cases is poor. Retrospective application of an existing clinical prediction rule was found to be highly accurate in identifying patients with positive CT findings. Prospective use of such a rule could reduce the number of CT scans ordered without missing significant injuries.
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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 | 0.005 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".