Radiation Exposure from the Patient Perspective: An Argument for the Inclusion of Dose History
Bibliographic record
Abstract
ABSTRACT: Patients in diagnostic imaging departments often ask about the risk of injury from x radiation. They are referred to wall posters or consent forms that declare (rightly) that the risk of harm from the proposed exam is very small and is far outweighed by the benefit. If a comparative risk value is provided, most likely it is based on a single exposure and derived from population estimates of cancer incidence and mortality. But is that information the most relevant for the patient? In a recent position statement, the AAPM recommends that only current exam risk should be considered, and that risk is independent of previous exams. We argue that if an exam carries risk of a negative event, the likelihood that a negative event occurred over all events increases with the number of exams. This cumulative risk, though still very small, must be a relevant consideration for health management.
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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 | 0.089 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.040 | 0.054 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".