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

As a unit, we implemented wireless image transfer from our Screening Mobile van in 2016, which resulted in some reduction in paper usage (only printing Client forms for those attending). However, the suspension of Screening at the start of the pandemic enabled us to undertake a multidisciplinary (radiological, radiographic and especially administrative) in-depth assessment of the existing pathway and design an equivalent electronic pathway, entirely without paper from screen to result. Redundant/duplicated paper-based processes (generally historical) were identified and expunged. The key has been the utilisation of the NBSS whiteboard (SAWB) as a 'nerve centre' to allow tracking of clinics waiting to be closed, read, and reconciled, as well as those with outstanding results. Processes were designed to allow. (a) communication of clinical findings between mammographer and reader (using 'Clinical Alert' system on NBSS), (b) verification of image checking on PACS (via a standalone database), (c) recording of abnormalities on PACS images (d) communication of assessment imaging requirements to the admin team.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.785
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
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 teacher head, 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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