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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 t 0 score 66.9 ± 8.9 vs. t 1 score 90.4 ± 11.7; p < 0.001), and decreased 3 months postintervention but remained elevated above baseline (t 2 scores 77.2 ± 11.5, compared to t 0 ; p = 0.03). Self‐reported confidence in performing UGNBs increased posttraining (t 0 5.0 ± 2.3 compared to t 1 score 7.1 ± 1.5; p = 0.002) but decreased to baseline levels 3 months postintervention (t 2 = 6.0 ± 1.9, compared to t 0 ; 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 teacher head, 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".

Quick stats

Citations12
Published2023
Admission routes1
Has abstractyes

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