Bedside ultrasound training using web-based e-learning and simulation early in the curriculum of residents
Bibliographic record
Abstract
BACKGROUND: Focused bedside ultrasound is rapidly becoming a standard of care to decrease the risks of complications related to invasive procedures. The purpose of this study was to assess whether adding to the curriculum of junior residents an educational intervention combining web-based e-learning and hands-on training would improve the residents' proficiency in different clinical applications of bedside ultrasound as compared to using the traditional apprenticeship teaching method alone. METHODS: Junior residents (n = 39) were provided with two educational interventions (vascular and pleural ultrasound). Each intervention consisted of a combination of web-based e-learning and bedside hands-on training. Senior residents (n = 15) were the traditionally trained group and were not provided with the educational interventions. RESULTS: After the educational intervention, performance of the junior residents on the practical tests was superior to that of the senior residents. This was true for the vascular assessment (94% ± 5% vs. 68% ± 15%, unpaired student t test: p < 0.0001, mean difference: 26 (95% CI: 20 to 31)) and even more significant for the pleural assessment (92% ± 9% vs. 57% ± 25%, unpaired student t test: p < 0.0001, mean difference: 35 (95% CI: 23 to 44)). The junior residents also had a significantly higher success rate in performing ultrasound-guided needle insertion compared to the senior residents for both the transverse (95% vs. 60%, Fisher's exact test p = 0.0048) and longitudinal views (100% vs. 73%, Fisher's exact test p = 0.0055). CONCLUSIONS: Our study demonstrated that a structured curriculum combining web-based education, hands-on training, and simulation integrated early in the training of the junior residents can lead to better proficiency in performing ultrasound-guided techniques compared to the traditional apprenticeship model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".