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Record W3120643540 · doi:10.1111/anae.15244

Contemporary training methods in regional anaesthesia: fundamentals and innovations

2021· review· en· W3120643540 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnaesthesia · 2021
Typereview
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersU.S. Department of Veterans Affairs
KeywordsMedicineRegional anaesthesiaCurriculumNarrative reviewNarrativeThe RenaissanceTraining (meteorology)Engineering ethicsMedical educationAnesthesiaPedagogyPsychologyIntensive care medicine

Abstract

fetched live from OpenAlex

Over the past two decades, regional anaesthesia and medical education as a whole have undergone a renaissance. Significant changes in our teaching methods and clinical practice have been influenced by improvements in our theoretical understanding as well as by technological innovations. More recently, there has been a focus on using foundational education principles to teach regional anaesthesia, and the evidence on how to best teach and assess trainees is growing. This narrative review will discuss fundamentals and innovations in regional anaesthesia training. We present the fundamentals in regional anaesthesia training, specifically the current state of simulation-based education, deliberate practice and curriculum design based on competency-based progression. Moving into the future, we present the latest innovations in web-based learning, emerging technologies for teaching and assessment and new developments in alternate reality learning systems.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.324
GPT teacher head0.470
Teacher spread0.146 · 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