Analysis of radioactive waste generated during the cyclotron production of <sup>99m</sup> Tc
Bibliographic record
Abstract
Abstract Past and prospective shortages of medical radioisotopes have driven recent developments in the direct production of 99m Tc via the 100 Mo(p,2n) 99m Tc reaction. The cyclotron-based production method has been shown to successfully produce 99m Tc, however trace impurities present in the enriched molybdenum target can also lead to the unintended creation of other radioisotopes which constitute waste. The isotopic composition of the waste has to be investigated in order to determine how it can be handled, transported and safely stored. In this article, we report which waste radioisotopes are created alongside 99m Tc during target irradiation. Results are based on the gamma spectroscopy of waste produced. Significant complexities in the emission spectra made automated identification of radioisotopes inaccurate; complexities were resolved using a manual radioisotope identification procedure. The impact of target composition, integrated beam current and duration of target irradiation on the waste produced was studied. Results indicate that an average of 0.059 ± 0.003 GBq of waste is generated per 1 GBq of 99m Tc produced. Two-thirds of the total waste activity produced was attributed to 99 Mo ( T 1/2 = 66 h) alone, while a total of fifty radioisotopes were found in the waste. Long-lived isotopes ( T 1/2 > 2 months) constituted only 1% of the total waste activity at end of beam (EOB). In conclusion, it was determined that the waste generated during cyclotron-based 99m Tc production was acceptably low for routine clinical production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".