Fractal heterogeneity of peripheral blood flow: Implications for hematogenous metastases
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To determine how inhomogeneities in blood perfusion might affect the number of metastases that develop within an individual with cancer. METHODS: Experiments with lung metastases in mice, involving 320 treatment groups and 3165 mice, were reviewed. Inhomogeneities in the distribution of metastases amongst identically treated mice were analyzed by calculating the relative dispersion and clumping index. RESULTS: The relative dispersion exhibited fractal self-similarity on change of scale, and paralleled the effects observed with pulmonary blood flow. Clustering of metastases was also apparent: a minority of mice developed relatively large numbers of metastases; a majority of mice developed few metastases. CONCLUSIONS: Clustering of lung metastases occurred within groups of identically treated mice, and could be attributed to inhomogeneous blood perfusion. Consequently, the number of metastases in any individual was highly variable and correlated only partly with malignant potential. Inhomogeneities in blood flow favored the development of relatively few metastases, such that solitary or nil metastasis should occur more frequently than expected from chance alone.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".