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Record W4403147312 · doi:10.1212/ne9.0000000000200166

Education Research: Feasibility, Reliability and Educational Value of Neuromuscular Ultrasound Training in a Canadian Neurology Residency Program

2024· article· en· W4403147312 on OpenAlexaffabout
Ahmed N. Alzaabi, Gurpreet Chaggar, Mohammed Hussain, Vijay Daniels, Grayson Beecher

Bibliographic record

VenueNeurology Education · 2024
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsWomen and Children’s Health Research Institute
Fundersnot available
KeywordsNeurologyReliability (semiconductor)Medical educationValue (mathematics)Residency trainingPsychologyMedicineMedical physicsPhysical therapyComputer scienceNeuroscienceMachine learning

Abstract

fetched live from OpenAlex

Background and Objectives: Point-of-care neuromuscular ultrasound (NMUS) is increasingly used in the evaluation of peripheral nervous system disorders; however, there remains a gap in education and training for neurology residents. We evaluated whether neurology residents can feasibly be trained in basic NMUS skills and nerve cross-sectional area (CSA) measurement and whether they value incorporation of this training into their curriculum. Methods: ) of the median, ulnar, and fibular nerve at multiple sites were independently performed on 3 healthy volunteers and compared with measurements obtained by the trainer. Results: across all sites). Comparing pretraining and posttraining survey responses, there was a significant increase in agreement that basic NMUS operational skills were obtained and in confidence in independently measuring each nerve. NMUS training was considered a valuable component of a neurology residency program curriculum (median rating: strongly agree). Discussion: Neurology residents across stages of training can acquire basic NMUS and CSA measurement skills of the median and ulnar nerves after 2 half-days of training and value incorporation of NMUS training in their educational curriculum.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.078
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.406
Teacher spread0.357 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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