Potential Ways to Address Shortage Situations of <sup>99</sup>Mo/<sup>99m</sup>Tc
Bibliographic record
Abstract
99mTc, the most common radioisotope used in nuclear medicine, is produced in a nuclear reactor from the decay of 99Mo. There are only a few aging nuclear reactors around the world that produce 99Mo, and one of the major contributors, the National Research Universal (Canada), ceased production on October 31, 2016. The National Research Universal produced approximately 40% of the world’s 99Mo supply, so with its shut down, shortages of 99Mo/99mTc are expected. Methods: Nuclear pharmacies and nuclear medicine departments throughout the United States were contacted and asked to provide their strategies for coping with a shortage of 99Mo/99mTc. Each of these strategies was evaluated on the basis of its effectiveness for conserving 99mTc while still meeting the needs of the patients. Results: From the responses, the following 6 categories of strategies, in order of importance, were compiled: contractual agreements with commercial nuclear pharmacies, alternative imaging protocols, changes in imaging schedules, software use, generator management, and reduction of ordered doses or elimination of backup doses. Conclusion: The supply chain of 99Mo/99mTc is quite fragile; therefore, being aware of the most appropriate coping strategies is crucial. It is essential to build a strong collaboration between the nuclear pharmacy and nuclear medicine department during a shortage situation. With both nuclear medicine departments and nuclear pharmacies implementing viable strategies, such as the ones proposed, the amount of 99mTc available during a shortage situation can be maximized.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".