A General Stochastic Model for Tumor Growth: Simulating Cardiac Tumor (Myxoma) Development
Bibliographic record
Abstract
Tumors, whether cancerous or benign, are among the most prominent problems of our time, and creating mathematical models to study, understand, and predict their behavior is extremely important.In this article, we created a general stochastic model to study the development of tumor size and diameter.The significance of our model is that it can study tumor growth in general it takes into consideration the number of capillaries that are inside the tumors and the quantity of blood that enters the tumor as well as the efficiency of the nutrients.We applied this model to simulate the development of a tumor called Myxoma, which is a tumor that grows in the heart.In the simulation of the growth of the Myxoma tumor volume over time in days, we found that tumor volume may reach 7.56 cm 3 which is consistent with experimental studies which confirm that the tumor volume grows between 3.053 and 7.238 cm 3 .As for the tumor diameter we have found that the change in diameter of tumor Myxoma ranges between 2 and 2.5 cm in a period of 360 days, and this is almost consistent with the real data where the size of the tumor diameter in the first year ranges between 1.8 and 2.4 cm.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".