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Record W4403377483 · doi:10.53555/sfs.v10i1.3095

Some Mathematical Models Of Cancer Growth And Tumor Cell-Immune Interaction: A Study Of Tumor-Immune Dynamics And Chemotherapy Applications

2023· article· en· W4403377483 on OpenAlexvenueno aff
Jitendra Singh

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldMathematics
TopicMathematical Biology Tumor Growth
Canadian institutionsnot available
Fundersnot available
KeywordsImmune systemChemotherapyCancerDynamics (music)Cancer chemotherapyTumor cellsMedicineCancer researchImmunologyInternal medicinePsychology

Abstract

fetched live from OpenAlex

There is a complex system that can be represented by mathematical modeling, and that system is the proliferation of cancer and its interaction with the immune system. The purpose of this work is to investigate the interactions between tumors and immune systems, with a particular emphasis on logistic growth models and an investigation into the equilibrium points of these models. In the following section, we will investigate the role that chemotherapy plays in relation to these equilibrium states as a therapeutic method. For the purpose of providing evidence for the theory and analysis, the research draws from a wide variety of sources. This highlights the usefulness of mathematical models in understanding the dynamics of the immune system and the formation of tumors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.125
GPT teacher head0.332
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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