The hierarchy of hazard controls in clinical magnetic resonance safety: an analysis of the American College of Radiology Manual on MR Safety
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it