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Record W2202712680 · doi:10.1038/bcj.2015.104

Prediction of response and survival in patients with chronic-phase chronic myeloid leukemia treated with omacetaxine mepesuccinate: logistic regression and landmark analyses

2015· letter· en· W2202712680 on OpenAlexaff
Meir Wetzler, Hagop M. Kantarjian, Franck E. Nicolini, Jeffrey H. Lipton, L Akard, Michele Baccarani, H. Jean Khoury, Elizabeth Li, Mihaela Munteanu, Jörge E. Cortes

Bibliographic record

VenueBlood Cancer Journal · 2015
Typeletter
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer Centre
FundersTeva Pharmaceutical Industries
KeywordsLogistic regressionMyeloid leukemiaMedicineOncologyInternal medicineHematologyImmunology

Abstract

fetched live from OpenAlex

Although many chronic myeloid leukemia (CML) patients initially do well with tyrosine kinase inhibitors (TKIs), some patients develop resistance or intolerance to multiple TKIs and need further therapy. Omacetaxine mepesuccinate (omacetaxine), a protein synthesis inhibitor, represents a new class of treatment that can produce major cytogenetic response (MCyR) in patients with CML who have developed resistance or intolerance to TKIs. The US Food and Drug Administration approved subcutaneous omacetaxine for treatment of CML in chronic-phase (CP) and accelerated-phase patients, with resistance or intolerance to two or more TKIs based on efficacy analysis of a subset of patients from two phase 2, open-label, international, multicenter studies.1, 2 Among the 76 evaluable patients with CML-CP in the efficacy analysis, MCyR was reported in 14 patients (18.4%), including confirmed complete cytogenetic response (CCyR) in six patients (7.9%), with a median MCyR duration of 12.5 months.2, 3 Median progression-free survival (PFS) and overall survival (OS) in CML-CP patients were 9.6 months (95% confidence interval (CI) 6.8–11.3 months) and 40.3 months (95% CI 23.8 months–not reached), respectively.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.041
GPT teacher head0.317
Teacher spread0.277 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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