A Presurvey and Postsurvey of a Web- and Simulation-Based Course of Ultrasound-Guided Nerve Blocks for Pediatric Emergency Medicine
Bibliographic record
Abstract
OBJECTIVES: Fracture pain in the pediatric emergency department generally is treated with systemic analgesia using opioids. Fracture pain can alternatively be controlled with ultrasound (U/S)-guided nerve blocks for which only minimal training is available to pediatric emergency medicine physicians. This study evaluated the effects of a Web- and half-day simulation-based U/S course. Outcome measures were physician comfort level with and intention to use U/S-guided nerve blocks in clinical practice. METHODS: We conducted a presurvey and postsurvey study targeting pediatric emergency medicine physicians. Participants completed a Web-based tutorial and a half-day simulation program. Participants completed survey questionnaires to document their comfort level and intention to use U/S-guided nerve blocks. Questionnaires were completed before, immediately after, and 1 month after course. RESULTS: Eleven physicians participated in the study. The participants' comfort with and intention to use U/S-guided ulnar and femoral nerve blocks increased immediately after course, but neither increase was sustained 1 month after course. Immediately following the course, participants reported that the course addressed their learning needs (91%) and that they would consider advanced training (91%). One month after course, participants reported that they would partake in refresher courses (82%), particularly if offered once per year (64%). CONCLUSIONS: This study suggests that Web- and simulation-based learning can increase comfort and intention to use U/S-guided nerve blocks and the need for follow-on training. Participants reported that their learning needs were met but that they would need annual refresher courses.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".