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Record W4307495412 · doi:10.1177/01926233221128755

Methodologies and Emerging Technologies for the Evaluation of the Hematopoietic System

2022· review· en· W4307495412 on OpenAlexaff
Florence Poitout‐Belissent, Allison Vitsky, Mark A. Smith, Madhu P. Sirivelu

Bibliographic record

VenueToxicologic Pathology · 2022
Typereview
Languageen
FieldMedicine
TopicHematological disorders and diagnostics
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsHematologyHaematopoiesisBone marrowContext (archaeology)Hematopoietic cellMedicineHematology analyzerMedical physicsPathologyInternal medicineComputer scienceStem cellBiology

Abstract

fetched live from OpenAlex

Hematology and bone marrow analysis is central to our understanding of the hematopoietic system and how it responds to insults, and this session presented during the 2022 STP symposium provided a review of current and novel approaches for the evaluation of the hematopoietic system in the context of nonclinical investigations. This publication summarizes the information presented on novel approaches for evaluation of the hematopoietic system using automated hematology analyzers, including details around the quantitative assessment of bone marrow cell suspensions as well as introducing several newly available hematology parameters. It was followed by a discussion on intravital microscopy and live cell imaging and how these methods can assist with de-risking hematopoiesis-associated safety concerns, and a review of recent assays using artificial intelligence for the evaluation of bone marrow.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.328
GPT teacher head0.462
Teacher spread0.134 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

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