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Record W4380788278 · doi:10.1002/lio2.1095

Competency‐based medical education in the United States: What the otolaryngologist needs to know

2023· review· en· W4380788278 on OpenAlexaff
Jenny Chen, Marc C. Thorne, Deepa Galaiya, Paolo Campisi, Stacey T. Gray

Bibliographic record

VenueLaryngoscope Investigative Otolaryngology · 2023
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOtorhinolaryngologyNeed to knowMedical educationMedicineComputer scienceSurgeryComputer security

Abstract

fetched live from OpenAlex

Competency-based medical education (CBME) is an outcomes-focused approach to educating medical professionals that will be central to future efforts to improve resident training in otolaryngology. The transition to CBME for otolaryngology in the United States will require the development of specialty-specific assessments and benchmarks, the financial and administrative support for implementation, the professional development of faculty and learners, and the cooperation of all major stakeholders in graduate medical education. In this article, we describe the need for evidence-based innovation in surgical training, the history of CBME in the United States, and the progress towards defining "entrustable professional activities" as the building blocks of assessments for CBME. We explore what such a paradigm shift in surgical education could mean for academic otolaryngologists by examining innovative educational practices in other surgical specialties and discussing foreseeable challenges in implementation for the American healthcare system.

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)0.005
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.583
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0030.014
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.001

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 head0.055
GPT teacher head0.392
Teacher spread0.336 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2023
Admission routes1
Has abstractyes

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