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Record W4280509433 · doi:10.3390/curroncol29050293

CAR T Cell Therapy for Chronic Lymphocytic Leukemia: Successes and Shortcomings

2022· review· en· W4280509433 on OpenAlexvenueno aff
Željko Todorović, Dusan Todorovic, Vladimir Marković, Nevena Ladjevac, Nataša Zdravković, Predrag Djurdjević, Nebojša Arsenijević, Marija Milovanović, Aleksandar Arsenijević, Jelena Milovanović

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
FundersFakultet Medicinskih Nauka, Univerziteta U KragujevcuMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsChimeric antigen receptorChronic lymphocytic leukemiaMedicineCAR T-cell therapyLeukemiaLymphomaCancer researchT cellCell therapyImmunologyCellImmune systemBiology

Abstract

fetched live from OpenAlex

Chimeric antigen receptor T (CAR T) cell therapy achieved remarkable success in B-cell leukemia and lymphoma which led to its incorporation in treatment protocols for these diseases. CAR T cell therapy for chronic lymphocytic leukemia (CLL) patients showed less success compared to other malignant tumors. In this review, we discuss the published results regarding CAR T cell therapy of CLL, possible mechanisms of failures and expected developments.

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)0.001
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.221
GPT teacher head0.485
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 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

Citations58
Published2022
Admission routes1
Has abstractyes

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