The Risk of Cancer from CT Scans and Other Sources of Low-Dose Radiation: A Critical Appraisal of Methodologic Quality
Bibliographic record
Abstract
INTRODUCTION: Concern exists that radiation exposure from computerized tomography (CT) will cause thousands of malignancies. Other experts share the same perspective regarding the risk from additional sources of low-dose ionizing radiation, such as the releases from Three Mile Island (1979; Pennsylvania USA) and Fukushima (2011; Okuma, Fukushima Prefecture, Japan) nuclear power plant disasters. If this premise is false, the fear of cancer leading patients and physicians to avoid CT scans and disaster responders to initiate forced evacuations is unfounded. STUDY OBJECTIVE: This investigation provides a quantitative evaluation of the methodologic quality of studies to determine the evidentiary strength supporting or refuting a causal relationship between low-dose radiation and cancer. It will assess the number of higher quality studies that support or question the role of low-dose radiation in oncogenesis. METHODS: This investigation is a systematic, methodologic review of articles published from 1975-2017 examining cancer risk from external low-dose x-ray and gamma radiation, defined as less than 200 millisievert (mSv). Following the PRISMA guidelines, the authors performed a search of the PubMed, Cochrane, Scopus, and Web of Science databases. Methodologies of selected articles were scored using the Newcastle Ottawa Scale (NOS) and a tool identifying 11 lower quality indicators. Manuscript methodologies were ranked as higher quality if they scored no lower than seven out of nine on the NOS and contained no more than two lower quality indicators. Investigators then characterized articles as supporting or not supporting a causal relationship between low-dose radiation and cancer. RESULTS: Investigators identified 4,382 articles for initial review. A total of 62 articles met all inclusion/exclusion criteria and were evaluated in this study. Quantitative evaluation of the manuscripts' methodologic strengths found 25 studies met higher quality criteria while 37 studies met lower quality criteria. Of the 25 studies with higher quality methods, 21 out of 25 did not support cancer induction by low-dose radiation (P = .0003). CONCLUSIONS: A clear preponderance of articles with higher quality methods found no increased risk of cancer from low-dose radiation. The evidence suggests that exposure to multiple CT scans and other sources of low-dose radiation with a cumulative dose up to 100 mSv (approximately 10 scans), and possibly as high as 200 mSv (approximately 20 scans), does not increase cancer risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".