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Record W4402601437 · doi:10.1148/ryai.230550

Assessing the Performance of Models from the 2022 RSNA Cervical Spine Fracture Detection Competition at a Level I Trauma Center

2024· article· en· W4402601437 on OpenAlexafffund
Zixuan Hu, Markand Patel, Robyn L. Ball, Hui Ming Lin, Luciano M. Prevedello, Mitra Naseri, Shobhit Mathur, Robert B. Moreland, Jefferson R. Wilson, Christopher D. Witiw, Kristen W. Yeom, Qishen Ha, D.A. Hanley, Selim Seferbekov, Hao Chen, Philipp Singer, Christof Henkel, Pascal Pfeiffer, Ian Pan, Harshit Sheoran, W. Li, Adam E. Flanders, Felipe Kitamura, Tyler Richards, Jason F. Talbott, Ervin Sejdić, Errol Colak

Bibliographic record

VenueRadiology Artificial Intelligence · 2024
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsUniversity of Toronto
FundersSt. Michael's Hospital Foundation
KeywordsCervical spineCenter (category theory)Trauma centerMedicineCompetition (biology)Internal medicineSurgeryRetrospective cohort study

Abstract

fetched live from OpenAlex

Winning machine learning models from the RSNA 2022 Cervical Spine Fracture Detection competition demonstrated high performance on a large clinical test set of emergency department cervical spine CT scans from a level I trauma center.

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)0.010
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.051
GPT teacher head0.294
Teacher spread0.243 · 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 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
Published2024
Admission routes2
Has abstractyes

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