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Record W4389608524 · doi:10.1111/bjh.19257

Evidence creation for myelofibrosis: Challenges and opportunities

2023· article· en· W4389608524 on OpenAlexaff
Vikas Gupta

Bibliographic record

VenueBritish Journal of Haematology · 2023
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMyelofibrosisMedicineHematologyGuidelineIntensive care medicineInternal medicinePathologyBone marrow

Abstract

fetched live from OpenAlex

Evidence-based guidelines for rare diseases, such as myelofibrosis (MF), continue to prove challenging to develop, and decision-making for MF is complex. The British Society for Haematology (BSH) has created a pragmatic symptom-guided risk-adapted framework on all aspects of management of MF and shared best practices on the use of JAK inhibitors, transplantation and other conventional therapies in the management of myelofibrosis. Commentary on: McLornan et al. The management of myelofibrosis: A British Society for Haematology Guideline. Br J Haematol 2024;204:136-150.

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)0.271
metaresearch head score (Gemma)0.637
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.271
Threshold uncertainty score0.899

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2710.637
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0080.010
Bibliometrics0.0100.006
Science and technology studies0.0060.024
Scholarly communication0.0220.046
Open science0.0130.021
Research integrity0.0550.064
Insufficient payload (model declined to judge)0.0200.006

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 head0.176
GPT teacher head0.345
Teacher spread0.168 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2023
Admission routes1
Has abstractyes

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