Introducing the fractional differentiation for clinical data-justified prostate cancer modelling under IAD therapy
Bibliographic record
Abstract
Actually the main motivation for the contents of this presentation is to introduce fractional calculus as a prospective mathematical tool for cancer dynamics, in particular prostate cancer modelling.In this context, rstly, our main problem on the controversial role of androgens for prostate cancer development is handled and according to our hypothesis a new mathematical model consisting of conventional logistic growth phenomena is constructed versus another prospective model based on a ecological phenomena, cell quota.Then, we compare these two models demonstrating the mean squared error (MSE) values for androgen and prostate-specic antigen (PSA) for the rst 1.5 cycles of intermittent androgen suppression (IAS) therapy administered to 62 selected patients from the Vancouver Prostate Center (Vancouver, BC, Canada).To reduce MSE values, we also generate the fractional version of the model and verify that fractional dierentiation provides better data tting for mathematical modelling.Moreover, with a discussion part, which hints for future works should be taken into account are pointed out.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".