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Record W4285395827 · doi:10.1182/blood.2022015849

Developing a classification of hematologic neoplasms in the era of precision medicine

2022· article· en· W4285395827 on OpenAlexaff
Mario Cazzola, Laurie H. Sehn

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsSpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)HematopathologyMechanism (biology)Precision medicineDiseaseComputer scienceBioinformaticsMedicinePathologyBiologyGenetics

Abstract

fetched live from OpenAlex

The recently developed International Consensus (IC) classification of hematologic neoplasms is primarily based on input from clinical advisory committees composed of pathologists, hematologists, oncologists, and genomic scientists. Morphology continues to represent a fundamental element in the definition of hematologic neoplasms. Acknowledging that the abnormal morphology is a result of dysregulated hematopoiesis driven by somatic gene mutations or altered expression, the IC classification considers genomic features more extensively. Defining nosologic entities based on underlying molecular mechanism(s) of disease is fundamental for enabling the development of precision treatments. Because translational and clinical research continuously advance the field, the classification of hematologic neoplasms will need to be regularly refined and updated; the basic question is what mechanism should be used for this purpose. Scientific hematopathology societies, in collaboration with hematology societies, should be primarily responsible for establishing a standing International Working Group, which would in turn collaborate with the World Health Organization (WHO)/International Agency for Research on Cancer (IARC) to realize and disseminate the classification. The current classification, with its strong morphology component, represents a basis for refinement. Through data sharing, the creation of large comprehensive patient data sets will allow the use of methods of inference, including statistical analyses and machine learning models, aimed at further identifying distinct disease subgroups. A collaborative clinico-pathologic review process will provide a mechanism for updating pathologic and genomic criteria within a clinical context. An interactive Web-based portal would make the classification more immediately available to the scientific community, while providing accessory features that enable the practical application of diagnostic, prognostic, and predictive information.

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)0.037
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.037
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0140.006
Science and technology studies0.0020.005
Scholarly communication0.0120.012
Open science0.0040.006
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0020.002

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 head0.022
GPT teacher head0.269
Teacher spread0.247 · 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 designTheoretical or conceptual
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

Citations36
Published2022
Admission routes1
Has abstractyes

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