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Record W4389304091 · doi:10.1016/s2352-3026(23)00305-8

Indication and management of allogeneic haematopoietic stem-cell transplantation in myelofibrosis: updated recommendations by the EBMT/ELN International Working Group

2023· review· en· W4389304091 on OpenAlexaff
Nicolaus Kröger, Andrea Bacigalupo, Tiziano Barbui, Markus Ditschkowski, Nico Gagelmann, Martin Grießhammer, Vikas Gupta, Nada Hamad, Claire Harrison, Juan Carlos Hernández‐Boluda, Steffen Koschmieder, Tania Jain, John Mascarenhas, Ruben A. Mesa, Uday Popat, Francesco Passamonti, Nicola Polverelli, Alessandro Rambaldi, Marie Robin, Rachel B. Salit, Thomas Schroeder, Bart L. Scott, Roni Tamari, Ayalew Tefferi, Alessandro M. Vannucchi, Donal P. McLornan, Giovanni Barosi

Bibliographic record

VenueThe Lancet Haematology · 2023
Typereview
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineMyelofibrosisInternational Prognostic Scoring SystemTransplantationHematopoietic stem cell transplantationInternal medicineDiseaseOncologyIntensive care medicineBone marrowMyelodysplastic syndromes

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.085
GPT teacher head0.356
Teacher spread0.272 · 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
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

Citations96
Published2023
Admission routes1
Has abstractno

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