Are Continued Efforts to Reduce Radiation Exposures from X-Rays Warranted?
Bibliographic record
Abstract
There are pressures to avoid use of radiological imaging throughout all healthcare due to the notion that all radiation is carcinogenic. This perception stems from the long-standing use of the linear no-threshold (LNT) assumption of risk associated with radiation exposures. This societal perception has led to relentless efforts to avoid and reduce radiation exposures to patients at great costs. Many radiation reduction campaigns have been launched to dissuade doctors from using radiation imaging. Lower-dose imaging techniques and practices are being advocated. Alternate imaging procedures are encouraged. Are these efforts warranted? Based on recent evidence, LNT ideology is shown to be defunct for risk assessment at low-dose exposure ranges which includes X-rays and CT scans. In fact, the best evidence that was once used to support LNT ideology, including the Life Span Study data, now indicates thresholds for cancer induction are high; therefore, low-dose X-rays cannot cause harm. Current practices are safe as exposures currently encountered are orders of magnitude below threshold levels shown to be harmful. As long as imaging is medically warranted, it is shown that efforts to reduce exposures that are within background radiation levels and that are also shown to enhance health by upregulating natural adaptive protection systems are definitively wasted resources.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".