Bibliographic record
Abstract
PURPOSE: Cancer Stem Cells (CSC) are hypothesised to influence tumour growth through their self-replication, cell loss, and differentiation into growth-limited cell types. A model for the random gain and loss of metastatic CSC is developed to investigate how the balance between these processes might affect metastatic efficiency, tumour involution and treatment response. MATERIALS AND METHODS: A stochastic birth-death model for metastasis was constructed for the replication and loss of CSC. The model was extended to account for single and sequential cancer treatments, with CSC repopulation. RESULTS: If CSC losses exceed gains, the metastasis would become extinct. The resultant extinction probability was greatest during a period of stochastic susceptibility; treatment could extend, or reestablish, this period. CONCLUSION: Random CSC losses, with 'seed and soil' selection, provided a mechanistic explanation for the involution of metastases, as well as for metastatic inefficiency. With such background losses, and the growth limitations of differentiated cells, a metastasis could take years to reach macroscopic size. The susceptibility period could be protracted, providing for a window for therapeutic opportunity. Metastases with a high background CSC loss would be more responsive to treatment than stabler metastases. Modulation of this loss could enhance the efficacy of conventional cancer treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".