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Record W2155933000 · doi:10.2174/138945010791320845

Emerging Concepts for the Treatment of Hematological Malignancies with Therapeutic Monoclonal Antibodies

2010· review· en· W2155933000 on OpenAlexfundno aff
Aude-Hélène Capietto, Samarh Keirallah, Emilie Gross, Nicolas Dauguet, Emilie Laprévotte, Christine Jean, Julie Gertner-Dardenne, Christine Bezombes, Anne Quillet‐Mary, Mary Poupot, Loïc Ysebaert, Laurent Guy, Jean‐Jacques Fournié

Bibliographic record

VenueCurrent Drug Targets · 2010
Typereview
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsnot available
FundersInstitute of Cancer ResearchInstitut National de la Santé et de la Recherche MédicaleInstitut National Du CancerAssociation pour la Recherche sur le Cancer
KeywordsRituximabMonoclonal antibodyMedicineLymphomaAntibodyTherapeutic approachCD20CancerImmunologyCancer researchInternal medicineDisease

Abstract

fetched live from OpenAlex

The development of therapeutic monoclonal antibodies (mAbs) has revolutionized the treatment of cancer along the last ten years. The best examples of their therapeutic efficacies have been obtained with rituximab for the treatment of CD20+ B-cell Non-Hodgkin Lymphoma (B-NHL), and several others antibodies with optimized bioactivities are now being developed for the treatment of various malignant hemopathies. We review here the main drugs developed in this field, and present some emerging concepts able to improve the bioactivities of the next generation of therapeutic mAbs.

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)0.000
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
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.003

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 head0.103
GPT teacher head0.432
Teacher spread0.329 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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