MétaCan
Menu
Back to cohort
Record W2942818276 · doi:10.1038/s41436-019-0459-4

Correction: BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors

2019· erratum· en· W2942818276 on OpenAlexaff
Andrew Lee, Nasim Mavaddat, Amber N. Hurson, Alex Cunningham, Tim Carver, Simon Hartley, Chantal Babb de Villiers, Á. Izquierdo, Jacques Simard, Marjanka K. Schmidt, Fiona M Walter, Nilanjan Chatterjee, Montserrat García‐Closas, Marc Tischkowitz, Paul D.P. Pharoah, Douglas F. Easton, Antonis C. Antoniou

Bibliographic record

VenueGenetics in Medicine · 2019
Typeerratum
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
FundersCancer Research UK
KeywordsRegretBreast cancerComputer scienceR packageMedicineCancerMachine learningInternal medicineProgramming language

Abstract

fetched live from OpenAlex

This has now been corrected in both the PDF and HTML versions of the Article. The authors regret this error.

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)0.007
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.138
Threshold uncertainty score0.463

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.122
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0040.003
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.1380.060

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 head0.010
GPT teacher head0.243
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations31
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueGenetics in MedicineSame topicBioinformatics and Genomic NetworksFrench-language works237,207