Bibliographic record
Abstract
Total body irradiation (TBI) is a standard procedure used in radiotherapy to eliminate any remaining cancerous cells following a chemotherapy treatment. However, there is no consensus regarding which TBI regimen (dose, dose rate, and fractionation) is the most efficient and least toxic. Also, we would like to know, how the TBI regime must be updated when it was interrupted by a natural disaster or blackout. ology: The Jones model of radiation-induced myelopoiesis is modified by adding new compartments for mutated and cancerous cells populations. This proposed carcinogenesis model is mathematically described by five non-linear coupled differential equations. Numerical and graphical solutions are obtained for U.S. and Canadian TBI regimens. To obtain stochastic solutions, transition rates that mediate the movement of cells among all compartments are replaced with random numbers. The developed algorithms and computational codes allowed us to quickly update a planned TBI regime after the patient's treatment was interrupted for a length of time. It is also showed that U.S. and Canadian TBI regimens killed about the same percentage of malignant cells (80%). The stochasticity procedure shows, on average, a mortality of about 83% of the malignant cells, which is in agreement with the deterministic solutions obtained for the US and Canadian TBI regimens. A stability analysis of the deterministic equations revealed only one equilibrium point, which is globally asymptotically stable. The proposed TBI compartmental model allows for a quick update of an interrupted TBI regimen, and a comparison among different TBI regimens.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".