Linear‐No‐Threshold Default Assumptions for Noncancer and Nongenotoxic Cancer Risks: A Mathematical and Biological Critique
Bibliographic record
Abstract
To improve U.S. Environmental Protection Agency (EPA) dose-response (DR) assessments for noncarcinogens and for nonlinear mode of action (MOA) carcinogens, the 2009 NRC Science and Decisions Panel recommended that the adjustment-factor approach traditionally applied to these endpoints should be replaced by a new default assumption that both endpoints have linear-no-threshold (LNT) population-wide DR relationships. The panel claimed this new approach is warranted because population DR is LNT when any new dose adds to a background dose that explains background levels of risk, and/or when there is substantial interindividual heterogeneity in susceptibility in the exposed human population. Mathematically, however, the first claim is either false or effectively meaningless and the second claim is false. Any dose-and population-response relationship that is statistically consistent with an LNT relationship may instead be an additive mixture of just two quasi-threshold DR relationships, which jointly exhibit low-dose S-shaped, quasi-threshold nonlinearity just below the lower end of the observed "linear" dose range. In this case, LNT extrapolation would necessarily overestimate increased risk by increasingly large relative magnitudes at diminishing values of above-background dose. The fact that chemically-induced apoptotic cell death occurs by unambiguously nonlinear, quasi-threshold DR mechanisms is apparent from recent data concerning this quintessential toxicity endpoint. The 2009 NRC Science and Decisions Panel claims and recommendations that default LNT assumptions be applied to DR assessment for noncarcinogens and nonlinear MOA carcinogens are therefore not justified either mathematically or biologically.
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 | 0.072 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.014 | 0.004 |
| Research integrity | 0.010 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".