Impact of Point-of-Care Ultrasound on Secondary Triage: A Pilot Study
Bibliographic record
Abstract
Abstract Objectives In mass casualty scenarios, patients with apparent hemodynamic and respiratory stability might have occult life-threatening injuries. These patients could benefit from more accurate triage methods. This study assessed the impact of point-of-care ultrasound (POCUS) on the accuracy of secondary triage conducted at an advanced medical post (AMP) to enhance the detection of patients who, despite their apparent clinically stable condition, could benefit from immediate life-saving interventions or priority earlier transport to definitive care. Methods A mass casualty simulated event consisting of a bomb blast in a remote area was conducted with 10 simulated casualties classified as YELLOW at the primary triage scene; patients were evaluated by 4 physicians at an AMP. Three patients had, respectively, hemoperitoneum, pneumothorax, and hemothorax. Two of the four physicians were provided the use of POCUS. Results All 4 physicians were able to suspect hemoperitoneum, but only physicians utilizing POCUS detected pneumothorax and hemothorax. Conclusion This study suggests that POCUS-enhanced secondary MCI triage at an AMP may represent an effective methodology to accurately detect nonapparent injuries that require time-dependent priority transport or life-saving interventions. Further studies with larger samples conducted in varied MCI scenarios are warranted.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".