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Record W4388459786 · doi:10.1111/bjh.19164

Diagnosis and evaluation of prognosis of myelofibrosis: A British Society for Haematology Guideline

2023· article· en· W4388459786 on OpenAlexfundno aff
Donal P. McLornan, Anna L. Godfrey, Anna Green, Rebecca Frewin, Siamak Arami, J.L. Brady, Nauman M. Butt, Catherine Cargo, Joanne Ewing, Sebastian Francis, Mamta Garg, Claire Harrison, Andrew J. Innes, Alesia Khan, Steven Knapper, Jonathan Lambert, Adam J. Mead, Andrew McGregor, Pratap Neelakantan, Bethan Psaila, Tim C. P. Somervaille, Claire Woodley, Jyoti Nangalia, Nicholas C.P. Cross, Mary Frances McMullin

Bibliographic record

VenueBritish Journal of Haematology · 2023
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastWellcome TrustAcademy of Medical SciencesEuropean Hematology AssociationNational Institute for Health and Care ResearchCancer Research UK
KeywordsMedicineGuidelineMyelofibrosisMEDLINEGrading (engineering)Family medicinePolycythaemiaClinical trialInternal medicinePathology

Abstract

fetched live from OpenAlex

This document represents an update of the British Society for Haematology (BSH) guideline on myelofibrosis (MF) first published in 2012 and updated in 2015. 1 This guideline aims to provide healthcare professionals with clear guidance on the diagnosis and prognostic evaluation of primary myelofibrosis (PMF), as well as post-polycythaemia vera myelofibrosis (post-PV MF) and post-essential thrombocythaemia myelofibrosis (post-ET MF).A section on prefibrotic MF is also included.A separate BSH Guideline covers the management of MF and is published alongside this guideline.

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)0.011
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0070.005
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0040.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0080.008

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 head0.050
GPT teacher head0.349
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2023
Admission routes1
Has abstractyes

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