False negative pericardial Focused Assessment with Sonography for Trauma examination following cardiac rupture from blunt thoracic trauma: a case report
Bibliographic record
Abstract
INTRODUCTION: The Focused Assessment with Sonography for Trauma examination is an invaluable tool in the initial assessment of any injured patient. Although highly sensitive and accurate for identifying hemoperitoneum, occasional false negative results do occur in select scenarios. We present a previously unreported case of survival following blunt cardiac rupture with associated negative pericardial window due to a concurrent pericardial wall laceration. CASE PRESENTATION: A healthy 46-year-old white woman presented to our level 1 trauma center with hemodynamic instability following a motor vehicle collision. Although her abdominal Focused Assessment with Sonography for Trauma windows were positive for fluid, her pericardial window was negative. After immediate transfer to the operating room in the setting of persistent instability, a subsequent thoracotomy identified a blunt cardiac rupture that was draining into the ipsilateral pleural space via an adjacent tear in the pericardium. The cardiac injury was controlled with digital pressure, resuscitation completed, and then repaired using standard cardiorrhaphy techniques. Following repair of her injuries (left ventricle, left atrial appendage, and liver), her postoperative course was uneventful. CONCLUSIONS: Evaluation of the pericardial space using Focused Assessment with Sonography for Trauma is an important component in the initial assessment of the severely injured patient. Even in cases of blunt mechanisms however, clinicians must be wary of occasional false negative pericardial ultrasound evaluations secondary to a concomitant pericardial laceration and subsequent decompression of hemorrhage from the cardiac rupture into the ipsilateral pleural space.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| 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".