A National Survey of Prehospital Care Services of United Kingdom for Use, Governance and Perception of Prehospital Point of Care Ultrasound
Bibliographic record
Abstract
Introduction: Point of care ultrasound (POCUS) has become a common practice in prehospital care over the last 10 years. There is lack of literature on its use and governance structure in United Kingdom (UK) prehospital care services. We aimed to survey the use, governance of prehospital POCUS among UK prehospital services and perceptions of clinicians and services regarding its utility and barriers to its implementation. Methods: Four electronic questionnaire surveys were delivered to UK helicopter emergency medical service (HEMS) & clinicians, ambulance and community emergency medicine (CEM) services between 1st of April and 31st of July 2021 investigating current use, governance structure for POCUS and perception about its benefits and barriers. Invitations were sent via email to medical directors or research leads of services and using social media. Survey links remained live for two months each. Results: Overall, 90%, 62% and 60% of UK HEMS, ambulance and CEM services respectively, responded to surveys. Most of the services used prehospital POCUS, however only two HEMS organisations fulfilled the Royal College of Radiology governance criteria for POCUS. The most commonly performed POCUS modality was echo in cardiac arrest. Majority of clinicians judged POCUS to be beneficial and the common perceived benefit was promotion of enhanced and effective clinical care. Major barriers to its implementation included a lack of formal governance, limited literature supporting its use and difficulties in performing POCUS in prehospital environment. Conclusion: This survey demonstrates that prehospital POCUS is being provided by a majority of the prehospital care services and clinicians have found it beneficial in providing enhanced clinical care to their patients. However, the barriers to its implementation are relative lack of governance structure and supportive literature.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".