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Record W2964332071 · doi:10.1002/mus.26642

Guidelines for neuromuscular ultrasound training

2019· article· en· W2964332071 on OpenAlexaff
Eman A. Tawfik, Michael S. Cartwright, Alexander Grimm, Andrea J. Boon, Antonios Kerasnoudis, David C. Preston, Einar Wilder‐Smith, Hubertus Axer, Lisa D. Hobson‐Webb, Nens van Alfen, Nicholas Crump, Nortina Shahrizaila, Peter Inkpen, Ross Mandeville, Sarada Sakamuri, Steven J. Shook, Susan Shin, Francis O. Walker

Bibliographic record

VenueMuscle & Nerve · 2019
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineUltrasoundDelphi methodPhysical medicine and rehabilitationMedical physicsNeuromuscular diseaseStandardizationPhysical therapyComputer scienceRadiologyPathologyArtificial intelligence

Abstract

fetched live from OpenAlex

Neuromuscular ultrasound has become an essential tool in the diagnostic evaluation of various neuromuscular disorders, and, as such, there is growing interest in neuromuscular ultrasound training. Effective training is critical in mastering this modality. Our aim was to develop consensus-based guidelines for neuromuscular ultrasound training courses. A total of 18 experts participated. Expert opinion was sought through the Delphi method using 4 consecutive electronic surveys. A high degree of consensus was achieved with regard to the general structure of neuromuscular ultrasound training; the categorization of training into basic, intermediate, and advanced levels; the learning objectives; and the curriculum for each level. In this study, a group of neuromuscular ultrasound experts established consensus-based guidelines for neuromuscular ultrasound training. These guidelines can be used in the development of the specialty and the standardization of neuromuscular ultrasound training courses and workshops.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.082
GPT teacher head0.334
Teacher spread0.252 · 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 designNot applicable
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

Citations36
Published2019
Admission routes1
Has abstractyes

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