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Record W4388408786 · doi:10.1212/ne9.0000000000200096

Education Research: Neuroradiology Curriculum and Competencies Among Canadian Adult Neurology Residency Programs

2023· article· en· W4388408786 on OpenAlexafffundabout
Diana Benea, Rose Di Ioia, Julien Bejjani, Anne Xuan-Lan Nguyen, Isabelle Hardy, Isabelle Trop, Nicolas Jodoin

Bibliographic record

VenueNeurology Education · 2023
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsUniversité de Montréal
FundersRoyal College of Physicians and Surgeons of Canada
KeywordsNeuroradiologyNeurologyCurriculumMedicineModalitiesMedical educationNeuroimagingRadiologyMedical physicsPsychologyFamily medicinePsychiatryPedagogy

Abstract

fetched live from OpenAlex

<h3>Background and Objectives</h3> While benefitting from neuroradiologists9 reports, neurologists use their own image interpretation to guide clinical decisions, especially in acute care settings. This calls for robust neuroradiology training in neurology residency, informed by current educational gaps and practices. This study aims to (1) characterize the formal neuroradiology curriculum among Canadian neurology residency programs; (2) assess neurology residents9 neuroimaging interpretation competencies; and (3) define neurology residents9 and program directors9 (PDs) attitudes toward the current curriculum and future directions. <h3>Methods</h3> Anonymous surveys were sent to Canadian neurology residents and PDs, querying neuroradiology learning activities, imaging modalities covered, assessment modalities, perceived residents9 competencies to interpret different modalities, and attitudes regarding neuroradiology training. Residents were asked to interpret 15 neuroimaging cases. Descriptive and inferential analyses were performed. Potential differences in residents9 interpretation success rates by seniority, self-perceived proficiency, and perception of curriculum sufficiency were examined using 2-tailed Welch tests with a 95% CI and Holm-Bonferroni comparison adjustment. Statistics were computed using Excel. <h3>Results</h3> Seventy-eight (32.6%) residents and 11 (68.8%) PDs participated. Ten of 11 PDs reported including a mandatory neuroradiology rotation, and 9/11 offered a formal neuroradiology curriculum covering head CT, head and neck CT angiography (CTA), spine MRI, and head MRI. Programs predominantly offered additional didactic lectures (9/11), teaching cases (8/11), and imaging websites (8/11). Most of the residents agreed with a minimum 1-month long rotation and desired regular didactics from neuroradiologists. Residents favored learning about head MRI (88.5%), head and neck CTA (76.9%), and spine MRI (69.2%). Senior residents9 self-perceived competencies were highest for head CT, head MRI, and head and neck CTA, but lower than PDs9 perception. Senior residents had greater interpretation scores than juniors (84.5% ± 13.2% vs 69.1% ± 19.9%; <i>p</i> &lt; 0.0001). Most PDs (7/11, 63.6%) expressed satisfaction with current curricula vs 32.1% of residents. PDs identified time and educator shortages as main barriers to increased training. <h3>Discussion</h3> Neuroradiology training varies among programs. Residents expressed strong interest in commonly taught modalities, for which they also expressed high self-perceived competencies. However, PDs expressed greater satisfaction than residents with the current training. Leveraging interactions with neuroradiologists and online case-based learning while emphasizing trainees9 interests can enhance postgraduate neuroradiology training for this useful skill.

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)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.045
GPT teacher head0.376
Teacher spread0.331 · 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.

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

Citations2
Published2023
Admission routes3
Has abstractyes

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