MétaCan
Menu
Back to cohort

Abstract B109: AZD5363, a catalytic pan-Akt inhibitor, in <i>Akt1</i> E17K mutation positive advanced solid tumors

2015· article· en· W2404352909 on OpenAlexaff
David M. Hyman, Lillian M. Smyth, Philippe L. Bédard, Amit M. Oza, Emma Dean, Anne Armstrong, João Paulo da Silveira Nogueira Lima, Hideaki Bando, Peter Kabos, José Alejandro Pérez Fidalgo, Kathleen N. Moore, Shannon N. Westin, Benoît You, Sarat Chandarlapaty, Leila Alland, Helen Ambrose, Andrew Foxley, Justin P.O. Lindemann, Martin Pass, Paul Rugman, Shaista Salim, Gaia Schiavon, Kenji Tamura, José Baselga, Udai Banerji

Bibliographic record

VenueMolecular Cancer Therapeutics · 2015
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsAKT1CancerProtein kinase BLibrary scienceMedicineChemistryPhosphorylationBiochemistryInternal medicineComputer science

Abstract

fetched live from OpenAlex

Abstract This abstract has been withheld from publication due to its inclusion in the AACR-NCI-EORTC Molecular Targets Conference 2015 Official Press Program. It will be posted online at the time of its presentation in a press conference or in a session: 10:00 AM ET Saturday, November 7. Citation Format: David M. Hyman, Lillian Smyth, Philippe L. Bedard, Amit Oza, Emma Dean, Anne Armstrong, Joao Lima, Hideaki Bando, Peter Kabos, J. Alejandro Perez-Fidalgo, Kathleen Moore, Shannon N. Westin, Benoit You, Sarat Chandarlapaty, Leila Alland, Helen Ambrose, Andrew Foxley, Justin Lindemann, Martin Pass, Paul Rugman, Shaista Salim, Gaia Schiavon, Kenji Tamura, Jose Baselga, Udai Banerji. AZD5363, a catalytic pan-Akt inhibitor, in Akt1 E17K mutation positive advanced solid tumors. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B109.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.309
Teacher spread0.281 · 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 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

Citations18
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueMolecular Cancer TherapeuticsSame topicRenal cell carcinoma treatmentFrench-language works237,207