Supplemental Figures and Tables from Efficacy of Cotargeting Angiopoietin-2 and the VEGF Pathway in the Adjuvant Postsurgical Setting for Early Breast, Colorectal, and Renal Cancers
Bibliographic record
Abstract
Supplemental Figure S1. Primary LM2-4luc16 breast tumors treated with CVX-2000, vehicle only, sunitinib, CVX-060, or sunitinib plus CVX-060. Supplemental Figure S2. Direct inhibitory effects of sunitinib and regorafenib on endothelial Tie2 vs. VEGFR2 tyrosine kinases. Supplemental Figure S3. Bioluminescent images from the LM2-4luc16 breast cancer adjuvant therapy experiments. Supplemental Figure S4. Primary orthotopic HCT116luc or HT29luc caecal tumors and HCT116luc-derived experimental liver metastases treated with regorafenib, CVX-060, or their combination. Supplemental Figure S5. Adjuvant regorafenib vs. CVX-060 therapies after resection of HCT116luc primary orthotopic caecal tumors. Supplemental Figure S6. Adjuvant regorafenib vs. CVX-060 therapies after resection of HT29luc primary orthotopic caecal tumors. Supplemental Figure S7. Bioluminescent images from the unresected RENCAluc metastatic renal cancer experiment. Supplemental Figure S8. Bioluminescent images from the resected RENCAluc renal cancer adjuvant therapy experiment. Supplemental Table S1. Randomized Phase III Clinical Trials of VEGF Pathway Inhibitors as Adjuvant (Postsurgical) Therapies for Resectable Cancers. Supplemental Table S2. Phase II/III Clinical Trials of Trebananib (AMG386, anti-Ang2/Ang1 peptibody) in Advanced or Metastatic Cancers. Supplemental Table S3. Relative inhibition of various kinases by pazopanib,sorafemib, sunitinib, vandetanib - data from the published literature. Supplemental Table S4. Relative binding affinity for various protein kinases estimated for several antiangiogenic tyrosine kinase inhibitors - data from the published literature. Supplemental Methods. Supplemental References.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.731 | 0.182 |
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".