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Record W2909446572 · doi:10.1080/10428194.2018.1562180

Entospletinib monotherapy in patients with relapsed or refractory chronic lymphocytic leukemia previously treated with B-cell receptor inhibitors: results of a phase 2 study

2019· article· en· W2909446572 on OpenAlexaff
Farrukh T. Awan, Michael J. Thirman, Dipti Patel‐Donnelly, Sarit Assouline, Arati V. Rao, Wei Ye, Brian T. Hill, Jeff P. Sharman

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineInternal medicineGastroenterologyRefractory (planetary science)Chronic lymphocytic leukemiaAdverse effectSykPhases of clinical researchLeukemiaOncologyChemotherapyTyrosine kinaseReceptor

Abstract

fetched live from OpenAlex

Entospletinib (GS-9973), an oral, selective inhibitor of spleen tyrosine kinase (SYK), was evaluated as monotherapy in this multicenter, phase 2 study (NCT01799889) of 49 patients with relapsed or refractory chronic lymphocytic leukemia (CLL), including those with Richter's transformation (RT), who had received prior therapy with a B-cell receptor (BCR) inhibitor. Patients were treated with entospletinib 400 mg BID as the starting dose. Sixteen patients achieved partial response and 21 had stable disease. The overall response rate was 32.7% (95% confidence interval [CI]: 21.7-45.3%). The median progression-free survival (PFS) was 5.6 (95% CI: 3.7-8.3) months. Twenty-one (of 43) patients (48.8%) experienced nodal response. Adverse events (AEs) occurred in all patients; most commonly fatigue, diarrhea, and anemia. Entospletinib monotherapy has clinical activity for patients with CLL and RT who have relapsed following therapy with BCR inhibitors.

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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.254
Teacher spread0.246 · 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 designNon-randomized trial
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

Citations37
Published2019
Admission routes1
Has abstractyes

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