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Record W2808357284 · doi:10.1038/s41588-018-0138-4

A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors

2018· article· en· W2808357284 on OpenAlexaff
Mariano J. Alvarez, Prem S. Subramaniam, Laura H. Tang, Adina Grunn, Mahalaxmi Aburi, Gabrielle E. Rieckhof, Elena V. Komissarova, Elizabeth Hagan, Lisa Bodei, Paul A. Clemons, Filemon S. Dela Cruz, Deepti Dhall, Daniel Diolaiti, Douglas A. Fraker, Afshin Ghavami, Daniel Kaemmerer, Charles Karan, Mark Kidd, Kyoung M. Kim, Hee C. Kim, Lakshmi P. Kunju, Ülo Langel, Zhong Li, Jeeyun Lee, Hai Li, Virginia A. LiVolsi, Roswitha Pfragner, Allison R. Rainey, Ronald Realubit, Helen Remotti, Jakob Regberg, Robert E. Roses, Anil K. Rustgi, Antonia R. Sepulveda, Stefano Serra, Chanjuan Shi, Xiaopu Yuan, Massimo Barberis, Roberto Bergamaschi, Arul M. Chinnaiyan, Tony Detre, Shereen Ezzat, Andrea Frilling, Merten Hommann, Dirk Jaeger, Michelle K. Kim, Beatrice S Knudsen, Andrew L. Kung, Emer Leahy, David C. Metz, Jeffrey W. Milsom, Young Shin Park, Diane Reidy‐Lagunes, Stuart L. Schreiber, Kay Washington, Bertram Wiedenmann, Irvin M. Modlin, Andrea Califano

Bibliographic record

VenueNature Genetics · 2018
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Center for Research ResourcesNational Institutes of HealthNational Cancer InstituteIrving Medical Center, Columbia UniversitySwedish Cancer Foundation
KeywordsRegulatorBiologyNeuroendocrine tumorsCancer researchMalignancyComputational biologyTranscriptomePrecision oncologyBioinformaticsPrecision medicineGeneticsGeneGene expressionEndocrinology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.371
Teacher spread0.342 · 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 designBench or experimental
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

Citations226
Published2018
Admission routes1
Has abstractno

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