Bibliographic record
Abstract
Mechanistic dynamic cell population models for the tumour control probability (TCP) to date have used a simplistic representation of the cell cycle: either an exponential cell-cycle time distribution (Zaider & Minerbo, 2000, Tumour control probability: a formulation applicable to any temporal protocol of dose delivery. Phys. Med. Biol., 45, 279-293) or a two-compartment model (Dawson & Hillen, 2006, Derivation of the tumour control probability (TCP) from a cell cycle model. Comput. Math. Methods Med., 7, 121-142; Hillen, de Vries, Gong & Yurtseven, 2009, From cell population models to tumour control probability: including cell cycle effects. Acta Oncol. (submitted)). Neither of these simplifications captures realistic cell-cycle time distributions, which are rather narrowly peaked around the mean. We investigate how including such distributions affects predictions of the TCP. At first, we revisit the so-called 'active-quiescent' model that splits the cell cycle into two compartments and explore how an assumption of compartmental independence influences the predicted TCP. Then, we formulate a deterministic age-structured model and a corresponding branching process. We find that under realistic cell-cycle time distributions, lower treatment intensities are sufficient to obtain the same TCP as in the aforementioned models with simplified cell cycles, as long as the treatment is constant in time. For fractionated treatment, the situation reverses such that under realistic cell-cycle time distributions, the model requires more intense treatment to obtain the same TCP.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".