Risk and Safety in Radiographic Utilization: Evaluating the Evidence From Current Research
Bibliographic record
Abstract
The 19 th ‐century discovery of radiographic imaging techniques revolutionized healthcare, allowing doctors to indirectly “see” inside their living patients. It was not until the mid‐20 th century that mutagenic effects from radiation exposure became a topic of concern. Since then, healthcare providers, scientists, and regulatory agencies have battled the question, “Does radiation exposure from radiographic imaging increase the risk of developing cancers and heritable conditions?” With no established answer, physicians must rely on the available evidence when assessing the risk versus benefit of having their patients x‐rayed. This review aims to gather and consolidate the available evidence so physicians can make informed decisions when ordering tests that use x‐rays. Popular topics such as the linear‐no‐threshold (LNT) model, radiation hormesis, and the as‐low‐as‐reasonably‐achievable (ALARA) principle are covered. Epidemiology studies, x‐ray physics, and related basic sciences are reviewed.
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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 | 0.052 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".