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Myelofibrosis: to transplant or not to transplant?

2016· review· en· W2558311129 on OpenAlexaff
Rebecca Devlin, Vikas Gupta

Bibliographic record

VenueHematology · 2016
Typereview
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMyelofibrosisMedicineHematopoietic cellTransplantationRuxolitinibDiseaseClinical trialTreatment modalityOncologyInternal medicineHaematopoiesisStem cellBone marrow

Abstract

fetched live from OpenAlex

Hematopoietic cell transplantation (HCT) is the only curative therapeutic modality for myelofibrosis (MF) at present. The optimal timing of HCT is not known in the presence of wider availability of less risky nontransplant therapies such as JAK 1/2 inhibitors. Careful review of patient, disease, and transplant-related factors is required in the appropriate selection of HCT vs the best available nontransplant therapies. We highlight some of the relevant issues and positioning of HCT in light of evolving data on JAK 1/2 inhibitors. The goal of this study is to provide the reader with updated evidence of HCT for MF, recognizing that knowledge in this area is limited by the absence of comparative studies between HCT and nontransplant therapies. Prospective studies are needed for better information on: the determination of optimal timing and conditioning regimens, the best way to integrate JAK inhibitors in the HCT protocols, and the impact of JAK inhibitors on graft-versus-host disease.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.070
GPT teacher head0.381
Teacher spread0.311 · 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

Citations45
Published2016
Admission routes1
Has abstractyes

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