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Record W3044364074 · doi:10.1093/ajcp/aqaa094

Integration of Genomic Medicine in Pathology Resident Training

2020· article· en· W3044364074 on OpenAlexaff
Richard L. Haspel, Jonathan R. Genzen, Jay Wagner, Christina M. Lockwood, Karen Fong, Lisa Browning, Allison M. Cushman‐Vokoun, Alix R D’Angelo, Marie C. DeFrances, Siddhartha Devarakonda, Pedro L. Fernández, Ruta Gupta, Neal I. Lindeman, Alexander Nobori, Erik Nohr, Jaqueline Payton, Keith Stringer, Chad Vanderbilt, Martin Young

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsUniversity of Calgary
FundersNational Cancer InstituteNational Institutes of Health
KeywordsMolecular pathologyGenomic medicineAnatomical pathologyGenomicsMedicinePathologyCellular pathologyBiologyComputational biologyGeneticsGenome

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess current pathology resident training in genomic and molecular pathology. METHODS: The Training Residents in Genomics (TRIG) Working Group has developed survey questions for the pathology Resident In-Service Examination (RISE) since 2012. Responses to these questions, as well as knowledge questions, were analyzed. RESULTS: A total of 2,529 residents took the 2019 RISE. Since 2013, there has been an increase in postgraduate year 4 (PGY4) respondents indicating training in genomic medicine (58% to approximately 80%) but still less than almost 100% each year for molecular pathology. In 2019, PGY4 residents indicated less perceived knowledge and ability related to both genomic and traditional molecular pathology topics compared with control areas. Knowledge question results supported this subjective self-appraisal. CONCLUSIONS: The RISE is a powerful tool for assessing the current state and also trends related to resident training in genomic pathology. The results show progress but also the need for improvement in not only genomic pathology but traditional molecular pathology training as well.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.085
GPT teacher head0.406
Teacher spread0.321 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2020
Admission routes1
Has abstractyes

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