The hierarchy of hazard controls in clinical magnetic resonance safety: an analysis of the American College of Radiology Manual on MR Safety
Bibliographic record
Abstract
OBJECTIVE: The purpose of this work was to critically assess safety guidance and practices in clinical magnetic resonance (MR) using the hierarchy of hazard controls (HHC). METHODS: Publicly available, widely used guidance documents for MR safety practice were gathered. The most recent guidance, the American College of Radiology (ACR) MR Safety Manual (2024) was selected for detailed analysis. A 5-point scale was assigned to the various levels in the hierarchy of hazard controls, from Elimination (score=5, most effective) to Personal Protective Equipment (score=1, least effective). MR safety practices recommended in the ACR MR Safety Manual were surveyed and scored using the 5-point scale. The safety practices were grouped by category of hazard addressed (e.g. main field, radio-frequency field, gradient field). RESULTS: Overall, Administrative Controls were the most common controls, followed by Engineering Controls. Controls within each hazard category featured a range of HHC scores, and all categories were predominantly served by Administrative Controls. CONCLUSION: The analysis presented in this work could serve as a tool to analyze choices made in the deployment of safety measures, to motivate decision- or policy-making, as a tool for assessment of MR safety programs, or as an approach to motivate future work in the design of hazard controls for MR.
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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 | 0.039 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".