TITLE: Computed Tomography: A Review of the Risk of Cancer Associated with Radiation Exposure
Bibliographic record
Abstract
Health care professionals order diagnostic imaging for their patients to help make a diagnosis, monitor treatment progress, determine the extent of a disease, or to reassure a patient of the absence of a disease. 1 One diagnostic modality that is both widely and increasingly being used in Canada and elsewhere is computed tomography (CT). 1 Other commonly used diagnostic imaging modalities are magnetic resonance imaging (MRI) and ultrasound (US). 2,3 Determining which diagnostic imaging test a patient should undergo requires the referring health care professional to consider factors such as diagnostic accuracy, cost, and patient safety. 1,4 For CT examinations, one primary area of interest of patient safety is the risk for radiationinduced cancer. 4,5 During CT examinations, cells are modified by the radiation which may cause these exposed cells to develop into cancer after a latency period. 6 The risk of cancer can also be passed on to offspring of the patient if the modified cells are in areas like the patient’s ovaries or testes. 6 The cancer that develops can be fatal or nonfatal and can appear after a long latency period (between 10 to 20 years for solid cancers) or a shorter latency period (two to five years for leukemia). 6 Radiation risk varies for many reasons, including the size of the patient, whether the patient is a
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".