Modeling of an Iterated Birth/Death Markov Process for Optimization of Radiotherapy Treatment Planning
Bibliographic record
Abstract
In this work, an iterated birth/death Markov process is modeled. Recent publications show that such a process can mimic the behaviour of clonogenic tumour cells exposed to fractionated radiation treatments. The model consists of a sequence of birth/death Markov chains, separated by radiation fractions. The destruction of tumour cells during a fraction of radiation is described by the linear-quadratic cell model. The stochastic behaviour of the cell population between radiation fractions is then described by a birth/death Markov process in order to determine how many clonogenic cells are present prior to the next fraction of radiation. Numerical analysis of the model was conducted with a tumour size of 109cells. Results from the model showed it would require a schedule of 27 radiation fractions at 2Gy per fraction delivered on every business day for a total of 38 days for the clonogenic population to reach zero. An advantage to such a model is that can be used to study both constant as well as variable radiation intervals and dosages. Model construction, validation, results and its applications in optimizing radiotherapy treatment planning are discussed
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".