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Record W2604822177 · doi:10.1007/s00277-017-2982-1

Advances in the treatment of relapsed/refractory chronic lymphocytic leukemia

2017· review· en· W2604822177 on OpenAlexaff
Chaim Shustik, Isabelle Bence‐Bruckler, Robert Delage, Carolyn Owen, Cynthia L. Toze, Steven Coutré

Bibliographic record

VenueAnnals of Hematology · 2017
Typereview
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsUniversity of British ColumbiaVancouver General HospitalOttawa HospitalBC Cancer AgencyUniversité LavalUniversity of CalgaryMcGill University Health Centre
Fundersnot available
KeywordsIbrutinibChemoimmunotherapyMedicineFludarabineVenetoclaxIdelalisibChronic lymphocytic leukemiaAlemtuzumabRituximabOncologyInternal medicineHematologyCyclophosphamideSalvage therapyTransplantationLeukemiaImmunologyLymphomaChemotherapy

Abstract

fetched live from OpenAlex

Treatment of chronic lymphocytic leukemia (CLL) has advanced with the introduction of chemoimmunotherapy (CIT) agents that have improved the outcomes of frontline therapy. However, most treated patients will relapse and require subsequent therapy. This review focuses on recent advances in the treatment of relapsed or refractory CLL. Until recently, treatment options for relapsed CLL were of limited efficacy. Retreatment with fludarabine, cyclophosphamide, and rituximab (FCR) was recommended for patients with a durable response to first-line FCR, although acquired genetic aberrations, impaired marrow reserve, and comorbidities often made this suboptimal therapy for many patients. New options include two agents targeting B cell receptor (BCR) signaling pathways (ibrutinib and idelalisib) and a B cell lymphoma-2 (BCL-2) inhibitor (venetoclax). Allogeneic hematopoietic stem cell transplantation (HSCT) remains a potentially curative option for younger patients with a suitable donor.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.185
GPT teacher head0.473
Teacher spread0.288 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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Same venueAnnals of HematologySame topicChronic Lymphocytic Leukemia ResearchFrench-language works237,207