Practical Aspects of 18F-FDG PET When Receiving 18F-FDG from a Distant Supplier
Bibliographic record
Abstract
UNLABELLED: With PET becoming more widely used, there is an increase in the number of imaging centers being forced to rely on distant suppliers of (18)F-FDG. Because of the large distances between major urban centers, this is particularly true for PET centers in Canada. METHODS: Our PET center, located in Winnipeg, Manitoba, Canada, currently purchases (18)F-FDG from a commercial vendor located more than 1,000 km from Winnipeg, necessitating transport by commercial airline cargo. This dependence on air transport and a distant supplier creates a situation in which our (18)F-FDG supply is less reliable than it would be with onsite production. In this article, we offer insight into the obstacles we have encountered in imaging with a distant supplier of (18)F-FDG and the solutions we have implemented to minimize the disruption to our patients and maximize the number of scans performed each year. RESULTS: The development of contingency plans and protocols designed to suit our operating environment has allowed us to increase the number of patient scans obtained from 659 in year 1 to 993 in year 3, an increase of 51%, despite an increase in our actual number of scan days of only 24%. (18)F-FDG injection timetables are presented for a variety of scenarios including normal delivery, low shipped activity, and delayed delivery. CONCLUSION: Through the careful establishment of contingency protocols and management of (18)F-FDG shipments, patient throughput can be increased and disruptions minimized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".