Feasibility and impact of a bespoke pre-hospital point of care ultrasound teaching and training programme at London’s air ambulance service
Bibliographic record
Abstract
Introduction: Point-of-care ultrasound has seen an increase in its use in pre-hospital emergency care. There is lack of literature around the training requirement for point-of-care ultrasound of pre-hospital clinicians. This service evaluation assesses the effectiveness of a bespoke hybrid teaching programme. Methods: This is a service evaluation of the point-of-care ultrasound teaching programme at London’s Air Ambulance from 1 April to 28 May 2021. Subjects’ knowledge, image interpretation and confidence were assessed at two different points. Data were gathered using REDCap and exported to Excel for analysis. Mean values and delta were calculated, and t-test was applied for statistical significance. Results: In total, 57 participants were included; out of which 11 were excluded, as they did not complete a post-course survey. Of these, 41.3% participants were point-of-care ultrasound naïve. Mean pre- and post-course scores were 76.5% and 81.7%, respectively, with an average delta improvement of 5.2% (95% confidence interval = 4.70%–5.68%) which was statistically significant ( p < 0.002). There was a statistically significant mean improvement of pre- and post-course scores between point-of-care ultrasound naïve and point-of-care ultrasound experienced groups ( p = 0.014). Confidence in using point-of-care ultrasound showed mean overall improvement from 2.36/4 to 3.34/4, a mean difference of 0.98 (95% confidence interval = 0.61–1.34), which was statistically significant ( p = 0.0039). Conclusion: Our service evaluation highlighted that a hybrid teaching model used by London’s Air Ambulance was feasible and had shown significant improvement in the knowledge, image interpretation and confidence of both the point-of-care ultrasound naïve and the PoCUS experienced cohort of clinicians.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".