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Record W2941758788 · doi:10.20517/cdr.2018.24

Pharmacogenetics of asparaginase in acute lymphoblastic leukemia

2019· review· en· W2941758788 on OpenAlexafffund
Rachid Abaji, Maja Krajinović

Bibliographic record

VenueCancer Drug Resistance · 2019
Typereview
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsUniversité de Montréal
FundersFondation Charles-BruneauCancer Research Society
KeywordsPharmacogeneticsAsparaginaseMedicineLymphoblastic LeukemiaAcute pancreatitisLeukemiaPharmacologyIntensive care medicineOncologyInternal medicineGenotypeBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Asparaginase is a key component in leukemias and lymphomas treatment protocols and is suggested as a treatment for other malignancies in which an amino acid depletion strategy is indicated. Asparaginase intolerance is subject to inter-individual variability and can manifest as hypersensitivity reactions, pancreatitis, thrombosis, as well as metabolic abnormalities, and may affect treatment outcome. Pharmacogenetics aims at enhancing treatment efficacy and safety by better understanding the genetic basis of variability and its effect on the pharmacological responses. Many groups tried to tackle the pharmacogenetics of asparaginase but the potential implementation of such findings remains debatable. In this review, we highlight the most important findings reported in studies of the pharmacogenetics of asparaginase related complications and treatment outcome in acute lymphoblastic leukemia.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.378
Teacher spread0.337 · 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 teacher head, not a consensus.

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

Citations16
Published2019
Admission routes2
Has abstractyes

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