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Record W1534491811 · doi:10.1002/9780470571224.pse258

Gene Expression Profiles as Preclinical and Clinical Cancer Biomarkers of Prognosis, Drug Response, and Drug Toxicity

2010· other· en· W1534491811 on OpenAlexaff
Jason A. Sprowl, Amadeo M. Parissenti

Bibliographic record

VenuePharmaceutical Sciences Encyclopedia · 2010
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsLaurentian University
Fundersnot available
KeywordsDrugChemotherapyMedicineToxicityOncologyDrug responseGeneGene expression profilingInternal medicineCancerDrug toxicityGene expressionBioinformaticsPharmacologyCancer researchBiologyGenetics

Abstract

fetched live from OpenAlex

Abstract Measurement of the expression level of specific genetic or protein biomarkers in patient serum or biopsies can be extremely valuable in the diagnosis and treatment of a variety of human neoplasms. However, tumor or serum level of large groups of proteins or transcripts is necessary to predict patient prognosis or outcome reliably after chemotherapy. This article focuses on the current progress made in the use of gene profiling to predict patient prognosis and response or toxicity to specific chemotherapy regimens.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.495
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.032
GPT teacher head0.393
Teacher spread0.361 · 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 designNot applicable
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

Citations0
Published2010
Admission routes1
Has abstractyes

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