Review of Radiation Dose Metric Tracking for Patients: Ethical Implications of the “Do Not Disclose” Standard
Bibliographic record
Abstract
Medical diagnostic imaging tests that produce ionizing radiation now deploy technology that captures an individual patient’s cumulative radiation dose. This raises the question of whether there is an imperative for regional health authorities to disclose this information to physicians who may then engage their patients in decisions about whether the potential harms are worth the benefits of subsequent diagnostic imaging. Currently, the advice of the professional bodies providing standards of practice for medical diagnostic imaging is to withhold this information from physicians. Their concern is that cumulative dose information is difficult to evaluate in terms of risk to individual patients; it is not easily applicable to clinical decision making about the appropriateness of a subsequent imaging exam; and referring clinicians will feel compelled to offer a patient a less efficacious non-ionizing test, which could negatively affect patient care. We present a critical analysis of several assumptions underlying the stance of non-disclosure. Working at the intersection of medical physics, medical anthropology, and clinical ethics, we offer an alternative framing of the discourse of risk that has shaped the recent scholarly debate on disclosure of individual cumulative radiation dose. We posit that a persuasive argument can be made against the stance of the professional bodies and for a policy of disclosure – provided that such a policy prioritizes patient-centred shared decision making, radiologists as risk-interpretation experts, and the authority of the prescribing physician.
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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 | 0.106 | 0.415 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".