Diagnostic and Prognostic Value of Focused Assessment With Sonography in Trauma (FAST) in Hemorrhage Stratification and Trauma Severity Classification
Bibliographic record
Abstract
Uncontrolled hemorrhage is the leading preventable cause of early death after trauma. The Focused Assessment with Sonography in Trauma (FAST) examination is widely used to detect free intraperitoneal fluid, but its prognostic value for hemorrhage grading and trauma severity classification requires synthesis. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. PubMed, Embase, Scopus, and Cochrane databases were searched through March 2025 for human studies assessing the diagnostic accuracy and prognostic utility of FAST in blunt or penetrating trauma. Studies were selected based on the population (or patient/problem), intervention, comparison (or control), and outcome (PICO) framework, excluding case reports, editorials, conference abstracts, animal studies, and those without extractable outcomes. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) and the Quality Assessment of Diagnostic Accuracy Studies, Version 2 (QUADAS-2) tool. Seven studies, including approximately 7,310 patients, met the inclusion criteria. FAST demonstrated high specificity up to 97% for detecting clinically significant hemoperitoneum. Positive findings were associated with increased transfusion requirements, greater need for surgery or embolization, higher intensive care unit (ICU) admissions, elevated injury severity, and higher short-term mortality. Semi-quantitative interpretations, such as the number and distribution of positive zones, enhanced hemorrhage stratification. Combining FAST with physiological parameters in validated trauma scoring systems improved the prediction of massive transfusion and operative urgency. Despite operator dependence and limited sensitivity for small or retroperitoneal bleeds, standardized protocols and serial assessments can strengthen its role in early trauma decision-making.
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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 | 0.032 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".