Bibliographic record
Abstract
2601 Objectives As low as reasonably achievable is a standard of practice that Nuclear Medicine Departments strive for. While low badge readings are desirable, badge readings must reflect the actual exposure to the staff. The annual review of Nuclear Medicine Technologist badge readings revealed conflicting data. Body badge readings were reasonable compared to the workload and historical data. Ring badge readings however, fell below the level of the body badge. Extremities tend to experience a higher exposure in Nuclear Medicine due to the handling of radiopharmaceuticals. The objective was to have ring badge readings more accurately reflect occupational exposure. Methods The low ring badge readings were first discussed with technologist staff during the annual Radiation Safety review. The decision was made by leadership to enact an educated, watchful waiting approach. The staff was reminded at a technologist meeting to comply with the badge policy. Nuclear Medicine leadership reviewed results after the first two quarters to evaluate compliance. The technologist staff was provided with anonymized results and individuals still not in compliance were counseled. Results were reviewed again once again after the third quarter and individuals once again counseled. Results The quarterly average for ring badge readings in 2014 was 60 mrem versus the quarterly average for body badges of 76 mrem. For first quarter 2015, ring badge readings rose to 119 mrem. By second quarter, the badge readings were 333 mrem. After repeat education and individual counseling, third quarter badge readings were 449 mrem. Individual results in the third quarter demonstrated two technologists with readings lower than expected. Badge readings will continue to be monitored for appropriateness. Conclusions An increase in badge readings would typically not be desirable, unless the readings are a more accurate reflection of practice. Body and ring badges should be worn when preparing or handling radiopharmaceuticals in order to ensure compliance with exposure limits set forth by the NRC. If badges are provided, they must be worn and worn properly to ensure an appropriate reflection of occupational exposure. Exposures are reasonably low in this department and staff had become indifferent to complying with the badge policy. This evaluation helped to support the principle of remaining vigilant, not only to abide by regulations but also to ensure a more accurate representation of extremity exposure. This is particularly important as patient volumes increase in areas such as PET/CT, especially with the introduction of very short-lived isotopes with higher activities. Accurate exposure results will help ensure that proper workflows and shielding designs have been created and adjustments to do not need to be made. Re-educating the staff and providing updates was sufficient to correct the behavior and return badge readings to a more expected range.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.070 | 0.069 |
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".