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Record W3120962895 · doi:10.1177/0846537120981581

Diagnostic Radiology Residency Assessment Tools: A Scoping Review

2021· review· en· W3120962895 on OpenAlexaff
Wendy Tu, Rebecca Hibbert, Mario Kontolemos, Wilfred Dang, Timothy J. Wood, Raman Verma, Matthew D. F. McInnes

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2021
Typereview
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineCompetence (human resources)Variety (cybernetics)CurriculumMedical educationScale (ratio)Inclusion (mineral)Grading (engineering)Medical physicsRadiologyComputer scienceArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

PURPOSE: The multifaceted nature of learning in diagnostic radiology residency requires a variety of assessment methods. However, the scope and quality of assessment tools has not been formally examined. A scoping review was performed to identify assessment tools available for radiology resident training and to evaluate the validity of these tools. METHODS: A literature search was conducted through multiple databases and on-line resources. Inclusion criteria were defined as any tool used in assessment of radiology resident competence. Data regarding residents, evaluators and specifics of each tool was extracted. Each tool was subjected through a validation process with a customized rating scale using the 5 categories of validity: content, response process, internal structure, relations to other variables, and consequences. RESULTS: evaluations were most commonly set in the United States (57%). In terms of validation, we found low adherence to modern integrated validity, with 34% of studies including a definition of validity. When specifically examining the 5 domains of validation evidence presented, most were either absent or of low rigor (70%). Only one study presented a modern definition of validation (3%, 1/35). CONCLUSION: We identified 35 evaluation tools covering a variety of competency areas. However, few of these tools have been validated. Development of new validated assessment tools or validation of existing tools is essential for the ongoing transition to a competency-based 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)0.004
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.107
GPT teacher head0.446
Teacher spread0.339 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2021
Admission routes1
Has abstractyes

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