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Record W2120629149 · doi:10.3109/10428194.2012.737917

A phase I/II study of sorafenib in combination with low dose cytarabine in elderly patients with acute myeloid leukemia or high-risk myelodysplastic syndrome from the National Cancer Institute of Canada Clinical Trials Group: trial IND.186

2012· article· en· W2120629149 on OpenAlexaffabout
David MacDonald, Sarit Assouline, Joseph Brandwein, Suzanne Kamel‐Reid, Elizabeth A. Eisenhauer, Stephen Couban, Stephen Caplan, Alison H. Foo, Wendy Walsh, Brian Leber

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsJuravinski Cancer CentrePrincess Margaret Cancer CentreUniversity Health NetworkMcGill UniversityJewish General HospitalQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsMedicineCytarabineInternal medicineSorafenibMyeloid leukemiaMyelodysplastic syndromesOncologyRashCancerLeukemiaBone marrow

Abstract

fetched live from OpenAlex

Sorafenib is active in patients with acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The National Cancer Institute of Canada (NCIC) Clinical Trials Group initiated a phase I/II study of the combination of sorafenib with cytarabine in older patients with AML or high-risk MDS who were unsuitable for intensive chemotherapy. FLT3 mutational status was determined in all patients. Twenty-one patients were enrolled (four MDS, 17 AML) with a median age of 77 years. The recommended phase II dose (RP2D) was cytarabine 10 mg bid days 1-10 and sorafenib 600 mg/day days 2-28. Dose-limiting toxicities were fatigue, sepsis and skin rash. Of 15 evaluable patients treated at the RP2D, two patients responded. The overall response rate for eligible patients was 10%. FLT3 mutations were found in only three patients. We conclude that this combination of sorafenib and cytarabine has limited activity in this unselected cohort of elderly patients with AML/MDS in which FLT3 mutations seemed underrepresented.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.326
Teacher spread0.296 · 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.

Study designRandomized 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

Citations45
Published2012
Admission routes2
Has abstractyes

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