A new approach for manufacturing and processing targets to produce 99mTc with cyclotrons
Bibliographic record
Abstract
The most important radioisotope for nuclear medicine is [Formula: see text]Tc. After the supply crisis of [Formula: see text]Mo starting in 2008, the availability of [Formula: see text]Tc became a worldwide concern. Alternative methods for producing the medical imaging isotope [Formula: see text]Tc are actively being developed around the world. The reaction [Formula: see text]Mo(p, 2n)[Formula: see text]Tc provides a direct route that can be incorporated into routine production in nuclear medicine centers that possess medical cyclotrons for production of other isotopes, such as those used for Positron Emission Tomography. This paper describes a new approach for manufacturing targets for the (p, 2n) nuclear reaction on [Formula: see text]Mo and the foundation for the subsequent commercial separation and purification of the [Formula: see text]Tc produced. Two designs of targets are presented. The targets used to produce [Formula: see text]Tc are subject to a number of operational constraints.They must withstand the temperatures generated by the irradiation, accommodate temperature gradients from cooling system of the target, must be resilient and must be easily post-processed to separate the [Formula: see text]Tc. After irradiation, the separation of Tc from Mo was carried out using an innovative two-step approach. The process described in this paper can be automated with modules that easily fit in standard production hot cells found in nuclear medicine facilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".