Abstract B47: Combating cancer with combination therapy in underserved populations
Bibliographic record
Abstract
Abstract Combination therapy is an emerging treatment modality that combines two or more agents to produce a therapeutic effect. The combined use of therapeutic agents enhance efficacy compared to the monotherapy approach because it characteristically targets main key pathways with a synergistic or an additive effect. This approach potentially reduces drug-resistance, while simultaneously producing therapeutic anti-cancer benefits, such as the potential in reducing tumor growth and metastasis, arresting the cell cycle, reducing cancer stem cell populations, and inducing apoptosis. The 5-year survival rate for most cancers are still quite low, and the process of developing a new anti-cancer drug is long and extremely time consuming. Therefore, new strategies that target the survival pathways while providing efficient and effective results, as well as affordability, are being considered. One such approach incorporates the testing of therapeutic agents initially used for the treatment of different diseases on cancer. This approach is effective primarily when the FDA approved agent targets similar pathways found in cancer. Because one of the drugs used in combination therapy is already FDA approved and requires less funding to research, overall costs of combination therapy research are reduced. This increases cost efficiency of therapy and reduces the price of treatment, benefiting the “medically underserved”. In addition, an approach that combines repurposed pharmaceutical agents with other therapeutics have shown promising results in mitigating tumor size and volume. Note: This abstract was not presented at the conference. Citation Format: Tina Homayouni, Reza Bayat Mokhtari, Albina Tyker, Parandis Kazemi, Zhenya Morgatskaya, Narges Baluch, Sara Dhalla, Shreya Rekhi, Sushil Kumar, Herman Yeger. Combating cancer with combination therapy in underserved populations. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr B47.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".