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Record W4233509539 · doi:10.1038/modpathol.2018.16

USCAP 2018 Abstracts: Kidney/Renal Pathology (1674–1718)

2018· article· en· W4233509539 on OpenAlexaff
Jason L. Hornick, Rhonda K. Yantiss, Laura W. Lamps, Cme Subcommittee, Steven D. Billings, Shree Sharma, Informatics Subcommittee, Raja R. Seethala, Ilan Weinreb, David Kaminsky, Andea Zubair Baloch, Olca Baştürk, Gregory R. Bean, Daniel J. Brat, Amy Chadburn, Ashley Cimino‐Mathews, James R. Cook, Carol Farver, Meera Hameed, Michelle S. Hirsch, Anna Marie Mulligan, Rish K. Pai, Vinita Parkash, Anil Deepa, Patil Lakshmi, Priya Kunju, John Reith Raja, R Seethala Kwun, Wah Wen, Narasimhan P. Agaram, Benjamin Adam, Siegfried Wagner, Verena Broecker, Vivette Agati, Drachenberg Cinthia, Alton B. Farris, Laurette Geldenhuys, Alex B. Magil, Volker Nickeleit, Parmjeet Randhawa, Michael Mengel, A Osamah, Lynn Badri, M. N. P. Cornell, Jorge Lao, Mariam Torres-Mo-, Alexander Priya, Jorge Reis Almeida, Justin Hsueh, Huma Fatima, Haichun Yang, Agnes B. Fogo, Komal Arora, Ahmed Elbakly, Roberto Barrios, Lillian W. Gaber, A. Osama Gaber, Luan D. Truong, Bren Davis, Jacob Whitman, Raymond C. Harris, Marco Delsante, Serena M. Bagnasco, Jonathan Levi, Naima Carter- Monroe, Avi Z. Rosenberg

Bibliographic record

VenueModern Pathology · 2018
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsSt. Paul's HospitalDalhousie UniversityUniversity of Alberta
Fundersnot available
KeywordsPathologyRenal pathologyKidneyMedicineInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.215
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.004
Science and technology studies0.0020.001
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.7850.630

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 head0.020
GPT teacher head0.272
Teacher spread0.252 · 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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractno

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