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Record W4307522818 · doi:10.1101/2022.10.21.22281368

A practical approach to curate clonal hematopoiesis of indeterminate potential in human genetic datasets

2022· preprint· en· W4307522818 on OpenAlexafffund
Caitlyn Vlasschaert, Taralynn Mack, J. Brett Heimlich, Abhishek Niroula, Md Mesbah Uddin, Joshua S. Weinstock, Brian Sharber, Alexander J. Silver, Yaomin Xu, Michael R. Savona, Christopher J. Gibson, Matthew B. Lanktree, Michael J. Rauh, Benjamin L. Ebert, Pradeep Natarajan, Siddhartha Jaiswal, Alexander G. Bick

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonImpactQueen's University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesMedical Research CouncilVanderbilt UniversityCanadian Institutes of Health ResearchLeukemia and Lymphoma SocietyAmerican Society of HematologyVanderbilt University Medical CenterAlexander and Margaret Stewart TrustLudwig Center at HarvardNational Institutes of HealthPew Charitable Trusts
KeywordsExomeExome sequencingBiologyBiobankPopulationGermlineGeneticsComputational biologyMutationMedicineGene

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.030
metaresearch head score (Gemma)0.124
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.124
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0060.004
Science and technology studies0.0020.001
Scholarly communication0.0040.001
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.003

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 head0.057
GPT teacher head0.380
Teacher spread0.323 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations58
Published2022
Admission routes2
Has abstractyes

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