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Record W2256505962 · doi:10.3324/haematol.2015.139717

Acute myeloid leukemia patients clinical response to idasanutlin (RG7388) is associated with pre-treatment MDM2 protein expression in leukemic blasts

2016· letter· en· W2256505962 on OpenAlexaff
Bernhard Reis, Lori Jukofsky, Gang Chen, Giovanni Martinelli, Hua Zhong, Wonhee So, Michael Dickinson, Mark W. Drummond, Sarit Assouline, Maneja Hashemyan, Michel Theron, Steven Blotner, Jae‐Hong Lee, Margaret Kasner, Sung‐Soo Yoon, Ruediger Rueger, Karen Seiter, S Middleton, Kevin R. Kelly, Norbert Vey, Karen Yee, Gwen Nichols, L.-C. Chen, William E. Pierceall

Bibliographic record

VenueHaematologica · 2016
Typeletter
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsFlow cytometryMdm2BiomarkerMedicineMyeloid leukemiaClinical trialOncologyInternal medicineCancer researchImmunologyBiologyCell culture

Abstract

fetched live from OpenAlex

In translational research described, we investigated biomarker expression by flow cytometry for MDM2 antagonist clinical response association in relapsed/refractory AML patients treated with idasanutlin-based therapy (Clinicaltrials.gov identifier: NCT01773408).

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.330
Teacher spread0.298 · 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 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

Citations91
Published2016
Admission routes1
Has abstractyes

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