Abstract A35: An integrative model of cancer disparities based on the calcium molecular theory of carcinogenesis
Bibliographic record
Abstract
Abstract One key question that comes to mind regarding cancer patterns is what factors account for the differentiated distribution of different cancer types in a given population? In other words, why is prostate cancer, for example, more prevalent among African Americans than in Caucasian Americans? Or why do native Eastern Asians record the highest incidence of gastric cancer worldwide? Beyond the commonly accepted genetic polymorphism to cancer vulnerability, this study is an in-depth exploration of the factors underpinning cancer distribution. Using a population health approach, an ecologic analysis was conducted, based on the current knowledge on the most prevalent cancer types. The calcium molecular theory of carcinogenesis, as published in our earlier work and presented at the Paris 2016 World Cancer Congress, served as basis for the analysis. As a result, cancer distribution appears to be shaped by a complex interaction between some key socioecologic determinants. Most importantly, we established that these determinants have a differentiated impact on intracellular calcium concentrations and trafficking. Thus, specific populations will be selectively vulnerable to a certain cancer type through a distinctive effect of their socioecologic characteristics on internal calcium. We conclude that calcium ion, already known as a cellular messenger, is also an environmental messenger. That molecule mediates the effects of external factors and their cellular responses, and this interaction accounts for cancer type distribution in the population at large. The work is published in two complementary papers: the first relates to cancer determinants while the second will discuss their impact on calcium metabolism. Note: This abstract was not presented at the conference. Citation Format: Bernard KADIO, Sanni Hashimi Yaya, Ajoy Basak, James Gomes, Koffi Djè, Christian Mesenge. An integrative model of cancer disparities based on the calcium molecular theory of carcinogenesis [abstract]. In: Proceedings of the Tenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2017 Sep 25-28; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2018;27(7 Suppl):Abstract nr A35.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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.
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".