Teaching ultrasound‐guided regional anesthesia remotely: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Ultrasound-guided regional anesthesia (UGRA) requires acquisition of new skills. Learning requires one-on-one teaching, and can be limited by time and mentor availability. We investigate whether the skills required for UGRA can be developed and subsequently assessed remotely using a novel online teaching platform. This platform was developed at the University of Toronto to teach laparoscopic surgery remotely and has been termed Telesimulation. METHODS: Anesthesia Site Chiefs at 10 hospitals across Ontario were sent a letter inviting their anesthesia teams to participate in an UGRA remote training program. Four to five anesthetists from each site were recruited from the first four hospitals expressing interest. Simulation models and ultrasound machines were set up at each location and connected via Skype(™) and web cameras with the Telesimulation center at our hospital. Training consisted of four online sessions and one offline lecture in order to teach an ultrasound-guided supraclavicular block. Participants were evaluated before and after training by on-site and off-site assessors using a validated Checklist and Global Rating Scale (GRS). RESULTS: Nineteen staff anesthetists were recruited. Post-training scores were significantly higher across both assessment tools, on-site (P < 0.001) and off-site training locations (P = 0.003). The inter-rater reliability between on-site and remote training site ratings was good for the Checklist (ICC = 0.672, 95% CI: 0.369-0.830) and excellent for the GRS (ICC = 0.847, 95% CI: 0.706-0.921). CONCLUSION: This study demonstrates that UGRA can be taught remotely. Future research will focus on comparing this method to on-site teaching and its application in resource-restricted countries.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".