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Record W2614021025 · doi:10.2217/fon-2017-0031

Venetoclax for the treatment of patients with chronic lymphocytic leukemia

2017· article· en· W2614021025 on OpenAlexaboutno aff
Jennifer L. Crombie, Matthew S. Davids

Bibliographic record

VenueFuture Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood Institute
KeywordsVenetoclaxTumor lysis syndromeMedicineChronic lymphocytic leukemiaNeutropeniaOncologyRituximabInternal medicineClinical trialLymphomaLeukemiaImmunologyChemotherapy

Abstract

fetched live from OpenAlex

Venetoclax is a potent, selective inhibitor of BCL-2, a key regulator of the intrinsic pathway of apoptosis. In preclinical studies, venetoclax bound to BCL-2 with high affinity and rapidly induced apoptosis in chronic lymphocytic leukemia (CLL) cells. In early-phase clinical trials in CLL, venetoclax treatment led to tumor lysis syndrome in some patients with a large tumor burden, but this risk was subsequently mitigated by a revised study design that included lower initial dosing with intrapatient dose ramp up and close tumor lysis syndrome monitoring and prophylaxis. Other toxicities, such as neutropenia and gastrointestinal adverse events, were manageable. Venetoclax monotherapy resulted in durable and deep responses in patients with relapsed, refractory CLL, including for those with deletion 17p, leading to the approval of venetoclax by the US FDA for relapsed or refractory deletion 17p CLL, and recently to additional approvals in Europe and Canada. Trials also suggest that venetoclax induces deeper and more durable responses when used in combination with rituximab, and combination studies with other agents are ongoing. Phase III trials are also underway, and will provide data on the efficacy and safety of venetoclax in combination with monoclonal antibodies and targeted therapies in larger patient populations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.334
Teacher spread0.313 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations8
Published2017
Admission routes1
Has abstractyes

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