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Record W2912646751 · doi:10.21037/hbsn.2019.01.06

Long term follow-up after liver transplantation from a JAK2 mutation positive donor

2019· article· en· W2912646751 on OpenAlexaff
Alejandro Lazo‐Langner, Peter Ainsworth, Vivian C. McAlister

Bibliographic record

VenueHepatoBiliary Surgery and Nutrition · 2019
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsWestern University
Fundersnot available
KeywordsEssential thrombocythemiaMyelofibrosisJanus kinase 2MedicineCalreticulinPolycythemia veraMutationMyeloproliferative neoplasmGermline mutationExonTransplantationCancer researchInternal medicineImmunologyGeneGeneticsBone marrowBiology

Abstract

fetched live from OpenAlex

Philadelphia-negative myeloproliferative neoplasms (MPN), including polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF), are characterized by the presence of mutations involving the Janus Kinase 2 ( JAK2 ), calreticulin ( CALR ) or myeloproliferative leukemia virus oncogene ( MPL ) genes (1). Of these, the JAK2 V617F gain-of-function mutation is the most frequently found and is present in >95% of patients with PV and in about 60% of those with ET or PMF whereas JAK2 exon 12 mutations are found in the remaining PV patients (2). JAK2 V617F somatic mutations have been detected in granulocytes and platelets and therefore they could potentially be transmitted to recipients of solid organ transplants (3) and in fact the presence of the JAK2 V617F mutation has been detected in otherwise healthy blood donors (4,5).

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2019
Admission routes1
Has abstractyes

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