MétaCan
Menu
Back to cohort
Record W4399976754 · doi:10.1038/s41684-024-01395-2

Model matchmaking via the Solve-RD Rare Disease Models & Mechanisms Network (RDMM-Europe)

2024· article· en· W4399976754 on OpenAlexaff
Kornelia Ellwanger, Julie A. Brill, Elke de Boer, Stéphanie Efthymiou, Ype Elgersma, Marynelle S Icmat, François Lecoquierre, Amanda G. Lobato, Manuela Morleo, Michela Ori, Ashleigh E. Schaffer, Antonio Vitobello, Sara Wells, Binnaz Yalcin, R. Grace Zhai, Marc Sturm, Birte Zurek, Holm Graeßner, Eva Bermejo, Teresinha Evangelista, Vincenzo Nigro, Rebecca Schüle, Alain Verloes, Han G. Brunner, Philippe M. Campeau, Paul Lasko, Olaf Rieß

Bibliographic record

VenueLab Animal · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsMcGill UniversityUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeThird Health ProgrammeMedical Research CouncilEuropean Commission
KeywordsDiseaseComputer scienceComputational biologyRare diseaseIntensive care medicineMedicineBiologyPathology

Abstract

fetched live from OpenAlex

In biomedical research, particularly for rare diseases (RDs), there is a critical need for model organisms to unravel the mechanistic basis of diseases, perform biomarker studies and develop potential therapeutic interventions. Within Solve-RD, an EU-funded research project with the aim of solving large numbers of previously unsolved RDs, the European Rare Disease Models & Mechanisms Network (RDMM-Europe) has been established.

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)0.014
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0040.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0410.013

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 head0.019
GPT teacher head0.242
Teacher spread0.223 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueLab AnimalSame topicGenomics and Rare DiseasesFrench-language works237,207