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Record W4296038315 · doi:10.1530/jme-22-0141

How retinoic acid and arsenic transformed acute promyelocytic leukemia therapy

2022· review· en· W4296038315 on OpenAlexaff
Victoria Korsos, Wilson H. Miller

Bibliographic record

VenueJournal of Molecular Endocrinology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsAcute promyelocytic leukemiaArsenic trioxideRetinoic acidRetinoic acid receptor alphaPromyelocytic leukemia proteinCancer researchLeukemiaFluorescence in situ hybridizationChromosomal translocationMedicineBiologyRetinoic acid receptorMolecular biologyGeneImmunologyGenetics

Abstract

fetched live from OpenAlex

Acute promyelocytic leukemia (APL) is associated with severe coagulopathy leading to rapid morbidity and mortality if left untreated. The definitive diagnosis of APL is made by identifying a balanced reciprocal translocation between chromosomes 15 and 17. This t(15;17) results in a fusion transcript of promyelocytic leukemia (PML) and retinoic acid receptor alpha (RARA) genes and the expression of a functional PML/RARA protein. Detection of a fused PML/RARA genomic DNA sequence using fluorescence in situ hybridization (FISH) or by detection of the PML/RARA fusion transcript via reverse transcriptase polymerase chain reaction (RT-PCR) has revolutionized the diagnosis and monitoring of APL. Once confirmed, APL is cured in over 90% of cases, making it the most curable subtype of acute leukemia today. Patients with low-risk APL are successfully treated using a chemotherapy-free combination of all-trans retinoic acid and arsenic trioxide (ATO). In this review, we explore the work that has gone into the modern-day diagnosis and highly successful treatment of this once devastating 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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.287
Teacher spread0.263 · 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 designOther design
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

Citations18
Published2022
Admission routes1
Has abstractyes

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