Ultrasound-guided regional anesthesia: feasibility and effectiveness of teaching via telesimulation in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Acute pain management in resource-poor countries remains a challenge. Ultrasound-guided regional anesthesia is a cost-effective way of delivering analgesia in these settings. However, for financial and logistical reasons, educational workshops are inaccessible to many physicians in these environments. Telesimulation provides a way of teaching across distance by using simulators and video-conferencing software to connect instructors and students worldwide. We conducted a prospective study to determine the feasibility of ultrasound-guided regional anesthesia teaching via telesimulation in Ethiopia. METHODS: Eighteen Ethiopian orthopedic and emergency medicine house staff participated in telesimulation teaching of ultrasound-guided femoral nerve block. This consisted of four 90-min sessions, once per week. Week 1 consisted of a precourse test and a presentation on aspects of performing a femoral nerve block, weeks 2 and 3 were live teaching sessions on scanning and needling techniques, and in week 4, the house staff undertook a postcourse test. All participants were assessed using a validated Global Rating Scale and Checklist. RESULTS: Participants were provided with a validated checklist and global rating scale as a pretest and post-test. The participants showed significant improvement in their test scores, from a total mean of 51% in the pretest to 84% in their post-test. CONCLUSIONS: Teaching ultrasound-guided regional anesthesia of the femoral nerve remotely via telesimulation is feasible. Telesimulation can greatly improve the accessibility of ultrasound-guided regional anesthesia teaching to physicians in remote areas.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".