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Diagnostic assay to assist clinical decisions for unclassified severe combined immune deficiency

2020· article· en· W3036240123 on OpenAlexafffund
Panojot Bifsha, Jennifer W. Leiding, Sung‐Yun Pai, A. Colamartino, Nicholas Hartog, Joseph A. Church, Benjamin Oshrine, Jennifer M. Puck, M. Louise Markert, Élie Haddad

Bibliographic record

VenueBlood Advances · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersNational Center for Advancing Translational SciencesNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsOrganoidImmune systemPhenotypeCD34ImmunologyPeripheral bloodMedicineCellular differentiationBiologyBioinformaticsStem cellNeuroscienceCell biologyGeneticsGene

Abstract

fetched live from OpenAlex

Key Points 3D organoid T-cell differentiation from a few hundred peripheral blood CD34+ cells was successfully achieved. 3D organoid T-cell differentiation could help physicians distinguish intrinsic from extrinsic defects underlying a clinical SCID phenotype.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.005

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 head0.035
GPT teacher head0.303
Teacher spread0.268 · 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 designBench or experimental
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

Citations41
Published2020
Admission routes2
Has abstractyes

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