MétaCan
Menu
Back to cohort
Record W4381326147 · doi:10.1186/s13058-023-01659-8

Meeting Abstracts from the British Society of Breast Radiology Annual Scientific Meeting 2022

2023· article· en· W4381326147 on OpenAlexaff
Anthony Aylwin, Helen Newman, Nisha Sharma, Mathew Wallis, Bridget Hilton, Karen Clements, Abeer M. Shaaban, Sarah E. Pinder, David Dodwell, Cliona Kirwan, Simon Lowes, Senthurun Mylvaganum, Janet Litherland, Elinor J. Sawyer, Hilary Stobart, Olive Kearins, Elena Provenzano, Joanne Dulson-Cox, Samantha Brace-McDonnell, Alastair M. Thompson, Eleanor Cornford, Jackie Walton, Isobel Gordon, George Ralli, Carolina Fernandes, Amy H. Herlihy, Gemma Greenall, Sally Collins, Michael Brady, Nerys Forester, Hina Faisal, Jennifer Royds, Gauripriya Babu, Gavin Loy, Alexandru Calciu

Bibliographic record

VenueBreast Cancer Research · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsSt. Thomas Hospital
FundersRoyal Commission for the Exhibition of 1851
KeywordsSurgical oncologyMedicineMedical physicsBreast cancerGeneral surgeryInternal medicineCancer

Abstract

fetched live from OpenAlex

screen-detected DCIS, taking other factors into account

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.359
Threshold uncertainty score0.914

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.3590.261

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 head0.032
GPT teacher head0.370
Teacher spread0.338 · 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.

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBreast Cancer ResearchSame topicRadiomics and Machine Learning in Medical ImagingFrench-language works237,207