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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 OpenAlexaff
Leora Witkowski, Nadine Demko, Elodie Petrecca, Marie Loncol, William D. Foulkes

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.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.007

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

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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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