Monitoring internal contamination from OEW of an 18F-FDG production plant: WBC or head counting setup?
Bibliographic record
Abstract
The production 18F-FDG for positron emission tomography (PET) has consistently increased over the past two decades. The risk of internal contamination at 18F-FDG production facilities exists. A setup for evaluation of the 18F-FDG activity incorporated into the OEW brain, called Head Counting System (HCS), was presented in previous works. In this study, the whole body counter setup (WBC) was evaluated for monitoring 18F incorporations. The Monte Carlo Virtual Software (VMC in-vivo) and the MCNPx code were used to assess the system calibration coefficient (CC). Three 18F distributions were simulated: i) uniformly distributed in soft tissue (UDST); ii) Na18F biodistribution (NAFB); and iii) 18F-FDG biodistribution (FDGB). The calibration coefficient of WBC was compared to the current head counting system CC under the same biodistribution conditions. The ICRP male reference voxelized phantoms (RCP_AM) was used in the simulations. The results showed that the WBC setup was more efficient than the head counter for all the studied 18F distributions: UDST = 1060 %, FDGB = 488 % and NAFB = 340 %. Despite this, especially for 18F-FDG, the possibility of bladder voiding before measurement can lead to considerable uncertainties when the WBC setup is used. On the other hand, bladder activity does not show great influence the calibration coefficient of the head counting system. Future work will evaluate the WBC sources of uncertainties in the measurement of 18F incorporated activity.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".