MétaCan
Menu
← Back to cohort
Record W4394265296 · doi:10.6084/m9.figshare.20495866

Additional file 3 of Diagnostic yield and clinical relevance of expanded genetic testing for cancer patients

2022· dataset· en· W4394265296 on OpenAlexaff
Ozge Ceyhan‐Birsoy, Gowtham Jayakumaran, Yelena Kemel, Maksym Misyura, Umut Aypar, Sowmya Jairam, Ciyu Yang, Yirong Li, Nikita Mehta, Anna Maio, Angela G. Arnold, Erin Salo‐Mullen, Margaret Sheehan, Aijazuddin Syed, Michael F. Walsh, Maria I. Carlo, Mark E. Robson, Kenneth Offit, Marc Ladanyi, Jorge S. Reis‐Filho, Zsofia K. Stadler, Liying Zhang, Alicia Latham, Ahmet Zehir, Diana Mandelker

Bibliographic record

VenueOpen MIND · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRelevance (law)Yield (engineering)Significance testingGenetic testingComputer scienceComputational biologyBiologyGeneticsStatisticsMathematicsMaterials sciencePolitical science

Abstract

fetched live from OpenAlex

Additional file 3: Table S3. Genes tested on MSK-IMPACT grouped based on their penetrance and inheritance type.

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)0.001
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.418
Threshold uncertainty score0.830

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.4180.053

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 head0.048
GPT teacher head0.353
Teacher spread0.304 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueOpen MIND→Same topicBRCA gene mutations in cancer→French-language works237,207→