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Record W4309048303 · doi:10.1097/ppo.0000000000000635

Antibody-Drug Conjugates in Myeloid Leukemias

2022· review· en· W4309048303 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Cancer Journal · 2022
Typereview
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsTrillium Therapeutics (Canada)
Fundersnot available
KeywordsGemtuzumab ozogamicinMyeloid leukemiaAntibody-drug conjugateMedicineCalicheamicinMonoclonal antibodyCancer researchTargeted therapyLeukemiaCytotoxic T cellDrugAntibodyImmunologyCancerOncologyPharmacologyCD33Internal medicineStem cellBiologyIn vitro

Abstract

fetched live from OpenAlex

ABSTRACT: Targeted therapy in oncology brings with it the promise to maximize cancer cell cytotoxicity with minimal off-target effects. Antibody-drug conjugates (ADCs), an important group of such targeted agents, consist of a monoclonal antibody conjugated to a potent cytotoxic drug. In the field of leukemia, ADCs form an important component of therapeutic arsenal through the use of gemtuzumab ozogamicin in acute myeloid leukemia and inotuzumab ozogamicin (InO) in B-cell acute lymphoblastic leukemia, 2 approved agents. A recombinant fusion protein, tagraxofusp, which function similar to ADC, has gained approval for therapy in blastic plasmacytic dendritic cell neoplasm. The use of such agents as monotherapy or as part of a combination therapy has led to improved response rates and outcomes in certain specific disease subtypes and has led to further studies to identify novel cellular targets and improved delivery of cytotoxic agents using ADC. In this review, we will discuss about ADCs in myeloid leukemia and understand their development and current use in the field.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0080.000

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 head0.101
GPT teacher head0.447
Teacher spread0.346 · 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