Radioisotope Shortages in Nuclear Medicine: How We Got There and Developing Solutions
Bibliographic record
Abstract
On May 18, 2009, Atomic Energy of Canada Limited (AECL) announced that the 52-year-old National Research Universal (NRU) reactor at Chalk River was out of service after detection of a heavy-water leak in the containment vessel, and they would not be able to supply several radioisotopes, most notably molybdenum 99 (Mo) used in the manufacture of Tc generators. Because approximately 40%e50% of the world’s supply of Mo was produced at the NRU reactor, this sudden loss threatened the provision of nuclear medicine studies to millions of patients around the world. There are approximately 30,000 nuclear medicine procedures performed every week in Canada at more than 200 nuclear medicine facilities and more than 15,000,000 every year in the United States. More than 70% of those procedures use Tc radiopharmaceuticals. By the end of May 2009, it became obvious that this would not be a shortterm problem, and, in August, the AECL announced that the NRU reactor would not return to service before Spring 2010. To further compound a bad situation, the second largest supplier of Mo in the world, the HFR-Petten reactor in the Netherlands was shut down for a 4-week routine maintenance in late July, which resulted in even more marked shortages into late August. Unfortunately, this was not the first prolonged shutdown of the NRU reactor. In late 2007, a dispute between the Canadian Nuclear Safety Commission (CNSC) and AECL caused an extended outage that eventually resulted in an act of Parliament (Bill C-38) to allow restarting of the NRU reactor. Subsequently, several reports examined the issues and problems surrounding those events, and recommendations were made to prevent a similar occurrence. A June 2008 report by Talisman International [1] laid the blame on a culture of informality and interactions that were ‘‘expert based’’ and not ‘‘process based.’’ A separate report of the Ad
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.022 | 0.042 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.023 | 0.037 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".