Clinician performed resuscitative ultrasonography for the initial evaluation and resuscitation of trauma
Bibliographic record
Abstract
BACKGROUND: Traumatic injury is a leading cause of morbidity and mortality in developed countries worldwide. Recent studies suggest that many deaths are preventable if injuries are recognized and treated in an expeditious manner - the so called 'golden hour' of trauma. Ultrasound revolutionized the care of the trauma patient with the introduction of the FAST (Focused Assessment with Sonography for Trauma) examination; a rapid assessment of the hemodynamically unstable patient to identify the presence of peritoneal and/or pericardial fluid. Since that time the use of ultrasound has expanded to include a rapid assessment of almost every facet of the trauma patient. As a result, ultrasound is not only viewed as a diagnostic test, but actually as an extension of the physical exam. METHODS: A review of the medical literature was performed and articles pertaining to ultrasound-assisted assessment of the trauma patient were obtained. The literature selected was based on the preference and clinical expertise of authors. DISCUSSION: In this review we explore the benefits and pitfalls of applying resuscitative ultrasound to every aspect of the initial assessment of the critically injured trauma patient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".