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Record W3080700916 · doi:10.1097/acm.0000000000003565

Selected Abbreviations Used in This Supplement

2020· article· en· W3080700916 on OpenAlexaboutno aff
J. M. McDonald

Bibliographic record

VenueAcademic Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationMedical educationGraduate medical educationCurriculumMedicineFaculty developmentHealth careProfessional developmentFamily medicinePsychologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

AAMC: Association of American Medical Colleges ACGME: Accreditation Council for Graduate Medical Education AHEC: area health education center AI: artificial intelligence AMA: American Medical Association AMEE: Association for Medical Education in Europe APN: Advanced Practice Nurse APRN: Advanced Practice Registered Nurse AV: audiovisual AY: academic year CACMS: Committee on Accreditation of Canadian Medical Schools CBL: case-based learning CBME: competency-based medical education CCC: clinical competency committee CK: Clinical Knowledge (USMLE Step 1 CK) CME: continuing medical education CPD: continuing professional development CQI: continuous quality improvement CS: Clinical Skills (USMLE Step 1 CS) CV: curriculum vitae DACA: deferred action for childhood arrivals DIO: designated institutional official EBM: evidence-based medicine ED: emergency department EPA: Entrustable Professional Activity EPO: educational program objective FQHC: federally qualified health center FTE: full-time equivalent GME: graduate medical education GQ: Graduation Questionnaire (AAMC) HSS: health systems science IAMSE: International Association of Medical Science Educators IHI: Institute for Healthcare Improvement IPE: interprofessional education IT: information technology IV: intravenous KSAs: knowledge, skills, and attitudes; or knowledge, skills, and abilities LCME: Liaison Committee on Medical Education LIC: longitudinal integrated clerkship MCQ: multiple-choice question MSOP: Medical School Objectives Project MSPE: Medical Student Performance Evaluation MSTP: Medical Scientist Training Program NBME: National Board of Medical Examiners NIH: National Institutes of Health NRMP: National Resident Matching Program OR: operating room OSCE: objective structured clinical examination PBL: problem-based learning PCRS: Physician Competency Reference Set PGY: postgraduate year PRIME: Program in Medical Education RVU: relative value unit SP: standardized patient TBL: team-based learning TSP: Teaching Scholars Program UME: undergraduate medical education USMLE: United States Medical Licensing Examination VA: Veterans Affairs VR: virtual reality VSAS: Visiting Student Application Service VSLO: Visiting Student Learning Opportunities WBA: workplace-based assessment

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.407
Threshold uncertainty score0.581

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.5930.392

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 head0.050
GPT teacher head0.378
Teacher spread0.327 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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