Validation protocol for current good manufacturing practices production of [15O]water for hybrid PET/MR studies
Bibliographic record
Abstract
INTRODUCTION: Oxygen-15 (O; t½ = 122.4 s) has been used for nuclear imaging experiments since the beginning of the field. With the advent of simultaneous hybrid PET/MR technology, [O]water has seen a resurgence and remains the gold standard method for quantitative blood flow studies. The short half-life presents a nontrivial challenge to applying current good manufacturing practices production methods to maintain patient safety. METHODS: A two-vial production method was devised to ensure adequate mixing of [O]water vapour into buffered isotonic saline. For production validation, six batches of [O]water were prepared: sterility, quality control testing and four patient doses. The final dose also underwent quality tested. Routine quality control testing included the following: radiochemical identity and purity, radionuclidic identity and purity, appearance, pH, pyrogenicity, and filter integrity. Sterility was retrospectively confirmed. For validation, breakthrough Pt concentration was also measured. RESULTS: Consistent yields of 10-12 GBq (270-325 mCi) were obtained 3 min after bombardment. Overall, 26 [O]water batches underwent quality control testing under this protocol and all met or exceeded release specifications for clinical use. CONCLUSION: The multiple batch protocol proved to be a safe and effective means for producing [O]water. Furthermore, this protocol could be readily adapted by any facility attempting to produce [O]water for clinical studies. Compared with previous attempts at our site, the protocol outlined here was more consistent and reliable, improved production workflow and led to more available radioactivity for participant injection and QC testing.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.020 |
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".