Impact of Point-of-Care Ultrasound on Secondary Triage: A Pilot Study
Bibliographic record
Abstract
Background/Introduction: In mass casualty incidents, patients with apparent hemodynamic and respiratory stability might have occult life-threatening injuries. These patients could benefit from more accurate evaluations to be sorted into triage categories for the purpose of treatment and priority transport decisions. 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, had simulated occult life-threatening injuries. Objectives: To determine if a WHO EMT could potentially benefit from the utilization of POCUS to guide lifesaving interventions and priority transport decisions. Method/Description: A mass casualty simulated incident consisting of a bomb blast in a remote area was conducted with 10 simulated casualties classified as YELLOW, delayed transport per Simple Triage and Rapid Treatment (START), at the primary triage scene. Patients were evaluated by 4 physicians at an AMP. Three patients had, respectively, hemoperitoneum, pneumothorax, and hemothorax. All physicians were competent in the use of POCUS to assess trauma patients. Two of the four physicians were provided the use of POCUS. Results/Outcomes: 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 non-apparent injuries that require life-saving interventions or priority transport. Further studies with larger samples conducted in varied MCI scenarios are warranted to provide the support for the WHO EMT to adopt POCUS as triage tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".