Bibliographic record
Abstract
PURPOSE: To describe the frequency distribution for the number of residual subclinical metastatic tumor cells after removal of the primary cancer. MATERIALS AND METHODS: Previously obtained autopsy, surgical pathological and laboratory data were used to characterize the size and number distributions for hematogenous and lymphatic metastases. Monte Carlo simulations were used to estimate the numbers of residual tumor cells based upon the assumption of a lognormal distribution for the sizes of metastases and Poisson, Poisson negative binomial, or negative binomial distributed numbers of metastases (corresponding to lymphatic metastases within individuals, hematogenous metastases within individuals, and lymphatic metastases within populations, respectively). RESULTS: In each of the scenarios the resultant distribution for the numbers of subclinical tumor cells was unimodal and positively skewed, with a tail extending to the higher numbers of metastases. When plotted with equal sized counting bins and according the logarithm of the number of tumor cells, the distributions showed deviations from the normal form no greater than several percentage points--a result considered acceptable given the variabilities inherent to metastasis data. CONCLUSIONS: The distribution for the number of residual subclinical metastases may be extrapolated from data and models derived from the size and number distributions for metastases. In the absence of a closed form description for this distribution, the lognormal distribution could provide a crude, but practical, approximation for cases limited to occult microscopic residual disease. These analyses will facilitate the definition of the dose-response for the adjuvant therapy of subclinical metastases.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".