MétaCan
Menu
← Back to cohort
Record W4232464483 · doi:10.3410/f.736011175.793573999

Faculty Opinions recommendation of Reanalysis of clinical exome sequencing data.

2020· dataset· en· W4232464483 on OpenAlexaff
Guy A. Rouleau

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2020
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsMontreal Neurological Institute and Hospital
FundersPeking Union Medical CollegeSchool of Medicine, Stanford UniversityPeking Union Medical College HospitalUniversity of Texas Health Science Center at HoustonJohns Hopkins UniversityMcGovern Medical SchoolCleveland ClinicChildren's Hospital of Philadelphia
KeywordsExome sequencingExomeCohortPrecision medicineMedicineProbandWorkloadData miningComputer scienceMedical physicsInternal medicineMutationGeneticsBiologyPathology

Abstract

fetched live from OpenAlex

A full list of authors and affiliations appears at the end of the article.The rapid accrual of knowledge in genomic medicine has prompted the reanalysis of preexisting data.1,2 We clinically reanalyzed data from two patient series that had undergone diagnostic proband-only exome sequencing.

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)0.004
metaresearch head score (Gemma)0.029
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 score0.550
Threshold uncertainty score0.642

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5500.383

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 head0.088
GPT teacher head0.414
Teacher spread0.326 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature→Same topicGenomics and Rare Diseases→French-language works237,207→