242 Quantitative measurements of occupational exposure to static magnetic stray fields from MRI scanners in clinical and research environments
Bibliographic record
Abstract
Objectives Workers’ exposure to static magnetic stray fields from MRI scanners was evaluated in a large cross-sectional study in The Netherlands. Factors determining the exposure to static magnetic fields (SMF) and time-varying magnetic fields (TVMF) like scanner characteristics, work patterns and individual practices could be quantified. Methods Occupational exposure to static magnetic fields (B) and rate of change of field due to movement through a static magnetic field (dB/dt) were measured using a Magnetic Field Dosimeter (University of Queensland). About 480 predominantly full-shift measurements were collected from more than 300 employees working at 14 clinical or research MRI facilities. During measurement days, participants kept a log of the tasks they performed and the scanner (s) at which they worked. Results Highest peak B and dB/dt exposures were observed among MRI radiographers and research staff. Peak exposures were highest in academic hospitals (mean [range]: 814 mT [36 - 4928]; 1291 mT/s [27 - 5057]) and lowest for people working in experimental animal imaging facilities (mean [range]: 227 mT [31 - 625]; 395 mT/s [32 - 1329]). Scanner field strength showed a strong association with peak B and dB/dt exposure when subjects worked near a closed bore scanner. However, for small bore scanners this association appeared to be negative. This could be partially explained by variability in shielding (active vs. passive) of the small-bore magnets. Conclusions For assessment of exposure for epidemiological studies classification solely based on scanner field strength is insufficient. The type of scanner (open/closed/extremity scanner; large/small bore) and type of shielding of the static magnetic field (active/passive) should be taken into account as well.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".