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Record W2148553912 · doi:10.1148/radiol.2471070418

Accuracy of Soft-Copy Digital Mammography versus That of Screen-Film Mammography according to Digital Manufacturer: ACRIN DMIST Retrospective Multireader Study

2008· article· en· W2148553912 on OpenAlexafffund
R. Edward Hendrick, Elodia B. Cole, Etta D. Pisano, Suddhasatta Acharyya, Helga S. Marques, Michael A. Cohen, Roberta A. Jong, Gordon E. Mawdsley, Kalpana M. Kanal, Carl J. D’Orsi, Murray Rebner, Constantine Gatsonis

Bibliographic record

VenueRadiology · 2008
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsUniversity of Toronto
FundersUniversity of North Carolina at Chapel HillThomas Jefferson UniversityMoffitt Cancer CenterUniversity of CincinnatiJohns Hopkins UniversityNorthwestern UniversityMassachusetts General HospitalUniversity of California, Los AngelesMemorial Sloan-Kettering Cancer CenterUniversity of WashingtonBrown UniversityNational Cancer InstituteUniversity of TorontoEmory UniversityUniversity of Pennsylvania
KeywordsMedicineMammographyDigital mammographyMedical physicsRadiologyReceiver operating characteristicInstitutional review boardBreast cancerCancerSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.005
metaresearch head score (Gemma)0.022
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.280
Teacher spread0.254 · 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

Citations39
Published2008
Admission routes2
Has abstractyes

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