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Record W4386285703 · doi:10.1148/ryai.230034

The RSNA Cervical Spine Fracture CT Dataset

2023· article· en· W4386285703 on OpenAlexaff
Hui Ming Lin, Errol Colak, Tyler Richards, Felipe Kitamura, Luciano M. Prevedello, Jason F. Talbott, Robyn L. Ball, Ekim Gümeler, Kristen W. Yeom, Mohammad Hamghalam, Amber L. Simpson, Jasna Strika, Deniz Bulja, Salita Angkurawaranon, Almudena Pérez Lara, María Isabel Gómez-Alonso, Johanna Ortiz Jiménez, Jacob Peoples, Meng Law, Hakan Doğan, Emre Altınmakas, Ayda Youssef, Yasser Mahfouz, Jayashree Kalpathy–Cramer, Adam E. Flanders, Nitamar Abdala, Michael Brassil, Priscila Crivellaro, Allison K. Duh, Fam Ekladious, Eduardo Moreno Júdice de Mattos Farina, Mohamed R. Gemae, Albert Huang, Omar Islam, Nedim Kruscica, Michael V. Kushdilian, Robin Lee, Zamir Merali, Robert B. Moreland, Shane Natalwalla, Oleksandra Samorodova, Baskaran Sundaram, Suradech Suthiphosuwan, Mónica Tafur, Donatella Tampieri, Jefferson R. Wilson, Christopher D. Witiw, Adil Zia, Gennaro D’Anna, Allison Grayev, Fátima Hierro, Michael D. Hollander, Ichiro Ikuta, Christie M. Lincoln, Lubdha M. Shah, Achint K. Singh, Nathan S. Doyle, Vikas Agarwal, Katie Bailey, Gagandeep Choudhary, Sammy Chu, Charlotte Chung, A.S.D. Costacurta, Muhammad Danial, Irene Dixe de Oliveira Santo, Venkata Naga Srinivas Dola, Kuang-chun Jim Hsieh, Adham Khalil, Neil Lall, Laurent Létourneau‐Guillon, David Russell Malin, Jeff Mason, Fanny Morón, Jaya Nath, Xuan V. Nguyen, Jacob W. Ormsby, Mark Oswood, Ö. Özsarlak, Samuel N Rogers, Jeffrey D. Rudie, Anousheh Sayah, Eric D. Schwartz, Loizos Siakallis, Neil Horner

Bibliographic record

VenueRadiology Artificial Intelligence · 2023
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsKingston Health Sciences CentreQueen's UniversityUniversity of TorontoSt. Michael's Hospital
FundersGenentechNational Institutes of HealthNational Cancer InstituteRadiological Society of North America
KeywordsCervical spineMedicineNuclear medicineFracture (geology)RadiologyGeologySurgeryGeotechnical engineering

Abstract

fetched live from OpenAlex

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. ©RSNA, 2023

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0180.026

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 head0.031
GPT teacher head0.301
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations26
Published2023
Admission routes1
Has abstractyes

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