Clinician-performed focused sonography for the resuscitation of trauma
Bibliographic record
Abstract
Traumatic death remains pandemic. The majority of preventable deaths occur early and are due to injuries or physiologic derangements in the airway, thoracoabdominal cavities, or brain. Ultrasound is a noninvasive and portable imaging modality that spans a spectrum between the physical examination and diagnostic imaging. It allows trained examiners to immediately confirm important syndromes and answer clinical questions. Newer technologies greatly increase the fidelity, accessibility, ease of use, and informatic manipulation of the results. The early bedside use of focused ultrasound as the initial imaging modality used to detect hemoperitoneum and hemopericardium in the resuscitation of the injured patient has become an accepted standard of care. Widespread dissemination of basic ultrasound skills and technology to facilitate this brings ultrasound to many resuscitative and critical care areas. Although not as widely appreciated, the focused use of ultrasound may also have a role in detecting hemothoraces and pneumothoraces, guiding airway management, and detecting increased intracranial pressure. Intensivists generally utilize a treating philosophy that requires the real-time integration of many divergent sources of information regarding their patients' anatomy and physiology. They are therefore positioned to take advantage of focused resuscitative ultrasound, which offers immediate diagnostic information in the early care of the critically injured.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".