Age-specific bone tumour incidence rates are governed by stem cell exhaustion influencing the supply and demand of progenitor cells
Bibliographic record
Abstract
Knudson's carcinogenic model, which simulates incidence rates for retinoblastoma, provides compelling evidence for a two-stage mutational process. However, for more complex cancers, existing multistage models are less convincing. To fill this gap, I hypothesize that neoplasms preferentially arise when stem cell exhaustion creates a short supply of progenitor cells at ages of high proliferative demand. To test this hypothesis, published datasets were employed to model the age distribution of osteochondroma, a benign lesion, and osteosarcoma, a malignant one. The supply of chondrogenic stem-like cells in femur growth plates of children and adolescents was evaluated and compared with the progenitor cell demand of longitudinal bone growth. Similarly, the supply of osteoprogenitor cells from birth to old age was compared with the demands of bone formation. Results show that progenitor cell demand-to-supply ratios are a good risk indicator, exhibiting similar trends to the unimodal and bimodal age distributions of osteochondroma and osteosarcoma, respectively. The hypothesis also helps explain Peto's paradox and the finding that taller individuals are more prone to cancers and have shorter lifespans. The hypothesis was tested, in the manner of Knudson, by its ability to convincingly explain and demonstrate, for the first time, a bone tumour's bimodal age-incidence curve.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".