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Record W4387516265 · doi:10.1002/aet2.10912

Implementing ultrasound‐guided nerve blocks in the emergency department: A low‐cost, low‐fidelity training approach

2023· article· en· W4387516265 on OpenAlexaff
Carrie D. Walsh, Irene Ma, Andrew Eyre, Munaa Dashti, Joseph Stegeman, Roger D. Dias, Arun Nagdev, Andrew Goldsmith, Nicole M. Duggan

Bibliographic record

VenueAEM Education and Training · 2023
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Calgary
FundersU.S. Department of DefenseNational Institutes of HealthNational Science Foundation
KeywordsMedicineEmergency departmentPhysical therapyConfidence intervalCompetence (human resources)Repeated measures designAnalysis of varianceInternal medicinePsychologyNursing

Abstract

fetched live from OpenAlex

Abstract Background Managing acute pain is a common challenge in the emergency department (ED). Though widely used in perioperative settings, ED‐based ultrasound‐guided nerve blocks (UGNBs) have been slow to gain traction. Here, we develop a low‐cost, low‐fidelity, simulation‐based training curriculum in UGNBs for emergency physicians to improve procedural competence and confidence. Methods In this pre‐/postintervention study, ED physicians were enrolled to participate in a 2‐h, in‐person simulation training session composed of a didactic session followed by rotation through stations using handmade pork‐based UGNB models. Learner confidence with performing and supervising UGNBs as well as knowledge and procedural‐based competence were assessed pre‐ and posttraining via electronic survey quizzes. One‐way repeated‐measures ANOVAs and pairwise comparisons were conducted. The numbers of nerve blocks performed clinically in the department pre‐ and postintervention were compared. Results In total, 36 participants enrolled in training sessions, eight participants completed surveys at all three data collection time points. Of enrolled participants, 56% were trainees, 39% were faculty, 56% were female, and 53% self‐identified as White. Knowledge and competency scores increased immediately postintervention (mean ± SD t0 score 66.9 ± 8.9 vs. t1 score 90.4 ± 11.7; p < 0.001), and decreased 3 months postintervention but remained elevated above baseline (t2 scores 77.2 ± 11.5, compared to t0; p = 0.03). Self‐reported confidence in performing UGNBs increased posttraining (t0 5.0 ± 2.3 compared to t1 score 7.1 ± 1.5; p = 0.002) but decreased to baseline levels 3 months postintervention (t2 = 6.0 ± 1.9, compared to t0; p = 0.30). Conclusions A low‐cost, low‐fidelity simulation curriculum can improve ED provider procedural‐based competence and confidence in performing UGNBs in the short term, with a trend toward sustained improvement in knowledge and confidence. Curriculum adjustments to achieve sustained improvement in confidence performing and supervising UGNBs long term are key to increased ED‐based UGNB use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.123
GPT teacher head0.412
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations12
Published2023
Admission routes1
Has abstractyes

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