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Record W2027611958 · doi:10.1016/j.leukres.2014.03.022

Lenalidomide and metronomic melphalan for CMML and higher risk MDS: A phase 2 clinical study with biomarkers of angiogenesis

2014· article· en· W2027611958 on OpenAlexaff
Rena Buckstein, Robert S. Kerbel, Matthew C. Cheung, Yuval Shaked, Lisa Chodirker, Christina R. Lee, Martha Lenis, Cindy Davidson, Mary‐Anne Cussen, Marciano D. Reis, Alden Chesney, Liying Zhang, Alexandre Mamedov, Richard A. Wells

Bibliographic record

VenueLeukemia Research · 2014
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsSunnybrook Health Science Centre
FundersCelgene
KeywordsLenalidomideMedicineMelphalanOncologyAngiogenesisInternal medicinePazopanibPharmacologyMultiple myelomaCancerSunitinib

Abstract

fetched live from OpenAlex

Metronomic, low dose chemotherapy may have anti-angiogenic effects and augment the effects of lenalidomide in MDS and CMML. We evaluated the clinical efficacy, tolerability and anti-angiogenic effects of melphalan 2mg and lenalidomide 10mg for 21 days/28 in CMML (n=12) and higher risk MDS (n=8) patients in a prospective phase II study. The primary endpoint was overall response and secondary endpoints included survival, progression-free survival, toxicity and biomarkers of angiogenesis. The median age was 73 years, 55% were pretreated and transfusion dependent. The overall response rate was 3(15%) of 19 evaluable patients but 25% in CMML and 33% in pCMML. Dose reductions and/or delays were common due to myelosuppression. Transient spikes in circulating endothelial cells that declined below baseline were seen in responders and patients with CMML, suggesting anti-angiogenic activity. In conclusion, lenalidomide and metronomic low dose melphalan demonstrate signals of clinical and possible anti-angiogenic activity in patients with pCMML that require future validation. This trial was registered at clinicaltrial.gov under # NCT00744536.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.446
Teacher spread0.356 · 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 designObservational
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

Citations20
Published2014
Admission routes1
Has abstractyes

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