Impact of Point‐of‐Care Ultrasound in Medicalized Prehospital Setting on Diagnostic Workup
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this study was to describe the indications for performing point-of-care ultrasound (POCUS) in prehospital settings. The secondary objective was to assess its impact on the diagnostic workup when its use was initiated by the emergency physician (EP) dispatched with a mobile intensive care unit (MICU). METHODS: This prospective observational cohort study was conducted within the MICU of Toulouse University Hospital from December 1, 2022, to May 31, 2023. All adult patients managed by the Toulouse MICU for whom the EP performed a POCUS examination were eligible for inclusion. EP was asked to state the diagnostic hypothesis being evaluated and to rate its likelihood before and after POCUS assessment. The hypothesis and the evaluation of the EP before and after POCUS were compared to the final diagnosis at hospital discharge. RESULTS: Over the 6-month study period, 83 had a POCUS by a MICU. The indications for performing POCUS were: high-energy trauma (n = 50 [60%]), chest pain (n = 20 [24%]), dyspnea (n = 9 [11%]), abdominal pain (n = 3 [4%]), and cardiac arrest (n = 1 [1%]). The diagnostic impression was more often consistent with the final diagnosis after POCUS than before (58 [70%] vs. 36 [43%]; P < .001). POCUS modified the diagnostic assessment wrongly in 7 (8%) patients and rightly in 28 (34%) patients. CONCLUSION: The most frequent indications for prehospital POCUS were high-energy trauma, chest pain, and dyspnea. POCUS improved the rate of initial diagnostic assessments consistent with the final diagnosis.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".