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Record W4324117491 · doi:10.1016/j.gimo.2023.100634

P587: Leveraging extensive datasets to better classify SMARCA4 variants*

2023· article· en· W4324117491 on OpenAlex
Leora Witkowski, Nadine Demko, Elodie Petrecca, Marie Loncol, William D. Foulkes

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGenetics in Medicine Open · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsSMARCA4Computer scienceComputational biologyData scienceBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Germline pathogenic variants (GPVs) in SMARCA4 cause rhabdoid tumor predisposition syndrome type 2 (RTPS2), which predisposes carriers to very specific rare tumors, such as small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) and malignant and atypical/teratoid rhabdoid tumors (MRT/ATRT). Although these cancers are relatively uncommon, they are extremely aggressive, with low long-term survival. While most of the pathogenic variants in SMARCA4 cause loss-of-function of the protein, a handful are missense variants, making classification difficult.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.058
GPT teacher head0.365
Teacher spread0.308 · 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