Rethinking Cancer Initiation, Promotion, and Progression: Senescence, Endopolyploidy and Meiosis as Players in a Stress-Induced Regenerative Cell Cycle
Bibliographic record
Abstract
Many definitions of cancer have been proposed over the years to explain cancer’s origins and pathophysiology, but the disease is too variable to allow any single description to completely encompass all of the myriad traits. A true, unifying theory of cancer must not only reveal how all known carcinogens contribute toward malignancy, but must also elucidate why cancers occur predominately in older patients, why they express so many similar behaviors, as well as account for each cancer’s individual variations. A conceptual reorganization of decades of cancer data may reveal the fundamental process that underlies the malignant transformation of cells, which not only provides a new understanding of the etiology of cancers, but also helps to interpret the subsequent events that occur after transformation, which is of significant clinical interest. The revised concepts of initiation, promotion, and progression suggest that cancer is a failed or incomplete attempt at regeneration that occurs when ordinary cellular events unintentionally coincide over time to force the use of a DNA repair pathway that is normally restricted for use in germ cells.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".