A mathematical investigation of different growth rates in simultaneous tumors at two distant sites
Bibliographic record
Abstract
Experimental data demonstrates that simultaneous injection of cancer cells at two distinct sites often results in one large and one small tumour. Unbalanced tumour-stimulating inflammation is hypothesized to be the cause of this growth rate separation, causing one tumour to grow faster than the other. Here, a mathematical model for immune recruitment and competition between two cancer sites is developed to explore the role of tumour-promoting inflammation in the observed growth rate separation. Due to the experimental set-up, immune predation may be neglected, focusing the model on tumour-promoting immune actions. A new mathematical model with localized immune recruitment and competition between the two cancer sites is developed using a multi-compartment ODE system. A simulated annealing algorithm is used to fit the model to control data (one tumour burden). Stability and parameter sensitivity analyses are used to explore the mathematical model and parameter space. Next, the two-tumour scenario is predicted by testing parameter values tied to possible biological mechanisms of action. The model predicts that indeed inflammation may be a contributor to growth rate separation observed in simultaneous tumour growth, if one site is pre-inflamed compared to the other.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".