Is one hand enough? Evaluation of the need for bilateral TLD extremity readings in a busy PET/CT imaging department
Bibliographic record
Abstract
2522 Objectives In many Functional Imaging departments it is common practice for PET technologists to wear only one extremity ring dosimeter on the dominant hand. The aim of this study is to compare the measured radiation dose received to the dominant and non dominant hands for PET technologists working in a busy stand-alone PET/CT imaging department (25 to 30 studies per day). Methods 7 full-time PET technologists (FTT) were issued a second thermoluminescence dosimeter (TLD) ring to wear on their non dominant hand. Extremity ring readings were collected and the results for the dominant and non dominant hands were compared. The rings were exchanged monthly and TLD extremity readings were obtained from Health Canada9s National Dosimetry Service (NDS). The radiation exposure values obtained from each hand were compared using a paired two-tailed t-test. Results Over a one month period evaluating FTTs (n=7), the dominant hand recorded an average exposure of 8.97 ± 2.43 mSv (mean ± st. dev), while the non dominant hand had an average exposure of 12.86 ± 4.83 mSv. The readings were significantly higher (p=0.025) for the non dominant hand, with a mean difference of 3.89 mSv (95% CI = 7.09 - 0.68). Conclusions PET departments should initially monitor TLD extremity readings for technologists on both hands to ensure annual extremity dose results are a reliable indication of maximum dose and within acceptable radiation exposure limits. If monthly reports reveal a significant difference between hands, department procedures and individual techniques should be adjusted. Given the significance of our initial findings, additional data will be collected to see if this trend continues
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".