SU‐FF‐T‐65: An Analytic Investigation of the Effect of Inter‐Patient Heterogeneity On Alpha/beta Ratio Estimates for Tumors
Bibliographic record
Abstract
Purpose: To analytically determine the relationship between the α/β ratio that would be obtained by fitting the individual (non‐averaged) tumor control probability (TCP) model to clinical data and the α/β estimate from a fit of the population‐averaged TCP model to the same clinical dose‐response dataset. Method and Materials: Recently, Carlone et al. (Med. Phys., 2006. 33(6): p. 1634–42) published fundamental forms of the population TCP model for the limits of dominant heterogeneity in radiosensitivity, and in clonogen number. In each case, the model is parameterized by γ50 and D50. The individual Poisson TCP model has also been expressed in terms of these geometric parameters. Since the functional forms of these TCP models are similar, approximately the same γ50 and D50 values would be obtained for each model if they were fit to the same clinical dataset. This fact allows us to determine mapping relationships between parameter ratio estimates obtained from fits using the averaged or non‐averaged TCP model. Mapping relationships are determined for the case of dominant heterogeneity in clonogen number, and in radiosensitivity. Results: When heterogeneity in clonogen number dominates a clinical dataset, the individual and population‐averaged α/β estimates are virtually identical. However, for the case where heterogeneity in radiosensitivity dominates, the individual α/β estimate will not be the same as the corresponding population α/β estimate. Conclusion: Heterogeneity in radiosensitivity is believed to be the dominant form among clinical datasets. Hence, our analytic expressions suggest the individual α/β ratio estimate should be different from the population estimate. Because of this ambiguity, we suggest that modelling has limited value in α/β determination; only the clinical hypofractionation trials will have the ability to validate the hypothesis originally put forth by Brenner and Hall (IJROBP, 1999. 43(5): p. 1095–1101) that prostate cancer responds to fractionation as does a late responding tissue.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".