Molybdenum target specifications for cyclotron production of<sup>99m</sup>Tc based on patient dose estimates
Bibliographic record
Abstract
In response to the recognized fragility of reactor-produced (99)Mo supply, direct production of (99m)Tc via (100)Mo(p,2n)(99m)Tc reaction using medical cyclotrons has been investigated. However, due to the existence of other Molybdenum (Mo) isotopes in the target, in parallel with (99m)Tc, other technetium (Tc) radioactive isotopes (impurities) will be produced. They will be incorporated into the labeled radiopharmaceuticals and result in increased patient dose. The isotopic composition of the target and beam energy are main factors that determine production of impurities, thus also dose increases. Therefore, they both must be considered when selecting targets for clinical (99m)Tc production. Although for any given Mo target, the patient dose can be predicted based on complicated calculations of production yields for each Tc radioisotope, it would be very difficult to reverse these calculations to specify target composition based on dosimetry considerations. In this article, a relationship between patient dosimetry and Mo target composition is studied. A simple and easy algorithm for dose estimation, based solely on the knowledge of target composition and beam energy, is described. Using this algorithm, the patient dose increase due to every Mo isotope that could be present in the target is estimated. Most importantly, a technique to determine Mo target composition thresholds that would meet any given dosimetry requirement is proposed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".