User:David MacQuigg/Sandbox/LNT

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Revision as of 18:33, 14 June 2024 by David MacQuigg (talk | contribs) (Created page with "{{Short description|Deprecated model predicting health effects of radiation}} {{Use dmy dates|date=October 2020}} 294px|thumb|right|Different assumptions on the extrapolation of the cancer risk vs. radiation dose to low-dose levels, given a known risk at a high dose:<BR />'''(A)''' supra-linearity, '''(B)''' linear<BR />'''(C)''' linear-quadratic, '''(D)''' hormesis The '''linear no-threshold model''' ('''LNT''') is a dose-response...")
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Different assumptions on the extrapolation of the cancer risk vs. radiation dose to low-dose levels, given a known risk at a high dose:
(A) supra-linearity, (B) linear
(C) linear-quadratic, (D) hormesis

The linear no-threshold model (LNT) is a dose-response model used in radiation protection to estimate stochastic health effects such as radiation-induced cancer, genetic mutations and teratogenic effects on the human body due to exposure to ionizing radiation. The model assumes a linear relationship between dose and health effects, even for very low doses where biological effects are more difficult to observe. The LNT model implies that all exposure to ionizing radiation is harmful, regardless of how low the dose is, and that the effect is cumulative over lifetime.

The LNT model is commonly used by regulatory bodies as a basis for formulating public health policies that set regulatory dose limits to protect against the effects of radiation. The model has also been used in the assessment of cancer risks of mutagenic chemicals. The validity of the LNT model, however, is disputed, and other models exist: the threshold model, which assumes that very small exposures are harmless, the radiation hormesis model, which says that radiation at very small doses can be beneficial, and the supra-linear model. It has been argued that the LNT model may have created an irrational fear of radiation.[1][2]

Government agencies generally support use of the LNT model.[3] However, many scientists now dispute the LNT assumptions that even low doses increase risk of cancer, and that all risk is simply additive, no matter how low the dose rate.[4]

Introduction

Origins

Increased Risk of Solid Cancer with Dose for A-bomb survivors, from BEIR report. Notably, this exposure pathway occurred from essentially a massive spike or pulse of radiation, a result of the brief instant that the bomb exploded, which while somewhat similar to the environment of a CT scan, is wholly unlike the low dose rate of living in a contaminated area such as Chernobyl, where the dose rate is orders of magnitude smaller. LNT does not consider dose rate and is an unsubstantiated one size fits all approach based solely on total absorbed dose. When the two environments and cell effects are vastly different. Likewise, it has also been pointed out that bomb survivors inhaled carcinogenic benzopyrene from the burning cities, yet this is not factored in.[5]
  1. (2016-06-01) "Epidemiology Without Biology: False Paradigms, Unfounded Assumptions, and Specious Statistics in Radiation Science (with Commentaries by Inge Schmitz-Feuerhake and Christopher Busby and a Reply by the Authors)". Biological Theory 11 (2): 69–101. DOI:10.1007/s13752-016-0244-4. PMID 27398078. PMC 4917595. Research Blogging.
  2. Cite error: Invalid <ref> tag; no text was provided for refs named WSJ
  3. The European Code has a plot of data that appears to be a direct confirmation of LNT for exposure to low levels of radon.
  4. Here are two scientific organizations that say LNT is bunk:
    HPS https://hps.org/hpspublications/historylnt/episodeguide.html
    SARI X-LNT https://www.x-lnt.org/evidence-for-radiation-hormesis
    The videos from Health Physics Society explore the reasons why governments have clung to LNT.
    The X-LNT website, sponsored by the Scientists for Accurate Radiation Information, has summaries of 7 studies supporting radiation hormesis, with links to the original studies.
  5. (April 2009) "The linear no-threshold relationship is inconsistent with radiation biologic and experimental data". Radiology 251 (1): 13–22. DOI:10.1148/radiol.2511080671. PMID 19332842. PMC 2663584. Research Blogging.