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Record W3153404381 · doi:10.1038/s41379-021-00756-3

Abstracts from USCAP 2021: Education (271-295)

2021· article· en· W3153404381 on OpenAlexaff
Jason Chair, Rhonda K. Yantiss, Kristin Jensen Chair, Cme Subcommittee, Laura C. Collins, Raja R. Seethala, Ilan Weinreb, Benjamin Adam, Rouba Ali‐Fehmi, Daniela Allende, Ghassan Allo, Isabel Alvarado‐Cabrero, Catalina Amador, Tatjana Antic, Roberto Barrios, Rohit Bhargava, Luiz Blanco, Jennifer M. Boland, Alain Borczuk, Elena F. Brachtel, Marilyn M. Bui, Eric Burks, Shelley Caltharp, Wendy Cao, Barbara A. Centeno, Joanna Chan, Jennifer R. Chapman, Yunn‐Yi Chen, Hui Chen, Wei Chen, Sarah Chiang, Nicole A. Cipriani, Beth Z. Clark, Alejandro Contreras, Claudiu Cotta, Jennifer Cotter, David Kaminsky, Zubair Baloch, Daniel J. Brat, Sarah Dry, William C. Faquin, Yuri Fedoriw, Karen Fritchie, Jennifer Gordetsky, Melinda Lerwill, Anna Marie Mulligan, Liron Pantanowitz, David Papke, Carlos Parra‐Herran, Rajiv M. Patel, Deepa T. Patil, Charles M. Quick, Lynette M. Sholl, Olga K. Weinberg, Maria Westerhoff, Michael Cho, Christopher C. Attaway, Malary Mani, Danielle Fortuna, Malvika Solanki, Carrie Bowler, Rondell P. Graham, Joseph J. Maleszewski, Loren Herrera Hernandez, Satyapal Chahar, Lomesh Choudhary, Ram Ahuja, Kalyan Sreeram, Anita Choudhary

Bibliographic record

VenueModern Pathology · 2021
Typearticle
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsYork University
Fundersnot available
KeywordsPathologyMedicine

Abstract

fetched live from OpenAlex

Background: Pathologic diagnoses are rooted in the art of describing and communicating. This art is an implicit part of pathology residency training, acquired through experiences but influenced by many factors. Our project assessed resident perceptions of their confidence to describe and communicate pathology and various impacting factors. Pathology Pyramid (PathP), a game-style session, complements the current curriculum with a team building, confidence-promoting exercise to strengthen this art.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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 designOther design
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

Citations0
Published2021
Admission routes1
Has abstractyes

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