Abstract B109: AZD5363, a catalytic pan-Akt inhibitor, in <i>Akt1</i> E17K mutation positive advanced solid tumors
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".