Bibliographic record
Abstract
PURPOSE: To evaluate the accuracy of focused abdominal ultrasonography (US) in detecting abdominal injuries that require in-hospital patient treatment in the setting of blunt abdominal trauma. MATERIALS AND METHODS: One thousand ninety patients with blunt abdominal trauma were assessed with focused abdominal US within 30 minutes of arrival at the hospital. Focused abdominal US results were positive if intra- or retroperitoneal fluid was detected. Patients with negative US results and no other major injuries were observed in the emergency department for 12 hours before discharge. Patients who deteriorated clinically after negative initial US underwent repeat US and/or emergency abdominopelvic computed tomography (CT). Patients with positive or indeterminate US results underwent emergency abdominopelvic CT. RESULTS: Nine hundred seventy-four (89%) patients had negative focused abdominal US results; eight of these underwent CT. Sixty-six (6%) had positive US results. Four (0.4%) had false-negative and 19 (1.7%) had false-positive US results. Twenty-seven (2.5%) had indeterminate US results; of these, five (18.5%) had positive CT results. One hundred twenty-four (11.4%) required emergency CT. After indeterminate cases were excluded, focused abdominal US had 94% sensitivity, 98% specificity, 78% positive predictive value, 100% negative predictive value, and 95% accuracy. CONCLUSION: Focused abdominal US has a high negative predictive value for major abdominal injury in patients with blunt abdominal 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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".