The Effectiveness of Focused Assessment With Sonography for Trauma in Evaluating Blunt Abdominal Trauma With a Seatbelt Mark Sign
Bibliographic record
Abstract
Background: Specific injury patterns have been recognized from seatbelt use including hollow viscous, mesenteric, and musculoskeletal injuries. We aimed to evaluate if focused assessment with sonography for trauma (FAST) is a reliable screening tool for the initial evaluation of the blunt abdominal trauma patient with a seatbelt sign. Methods: A retrospective review of adult trauma patients with blunt abdominal trauma and a positive seatbelt sign were evaluated over a three-year period. Data collected included age, gender, Glasgow coma scale (GCS), presence or absence of abdominal tenderness, results of diagnostic studies, operative findings, missed injuries, and mortality. Results: A total of sixty-nine patients were evaluated. Fifty-eight ultrasound scans were interpreted as negative and 11 positive. Three of the 11 were taken immediately to the operating room. The remaining 8 underwent computerized tomography (CT) according to protocol and clinical management was altered in two. Sixteen patients with a negative ultrasound examination underwent CT. Our series revealed 11 true and no false positives, as well as 54 true and 4 false negatives. The sensitivity of utilizing FAST for detecting a clinically significant injury in this study is 73% with 100% specificity, a negative predictive value of 93%, positive predictive value of 100%, and accuracy of 94%. Conclusions: The use of FAST, not as a single diagnostic modality, but as a screening tool with selective use of CT, is a relatively reliable instrument for the initial evaluation of the blunt abdominal trauma patient with a seatbelt mark sign. J Curr Surg. 2014;4(1):17-22 doi: http://dx.doi.org/10.14740/jcs207w
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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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".