Effective Dose in Nuclear Medicine Studies and SPECT/CT: Dosimetry Survey Across Quebec Province
Bibliographic record
Abstract
The aims of the current study were to draw a portrait of the delivered dose in selected nuclear medicine studies in Québec province and to assess the degree of change between an earlier survey performed in 2010 and a later survey performed in 2014. Methods: Each surveyed nuclear medicine department had to complete 2 forms: the first, about the administered activity in selected nuclear medicine studies, and the second, about the CT parameters used in SPECT/CT imaging, if available. The administered activities were converted into effective doses using the most recent conversion factors. Diagnostic reference levels were computed for each imaging procedure to obtain a benchmark for comparison. Results: The distributions of administered activity in various nuclear medicine studies, along with the corresponding distribution of the effective doses, were determined. Excluding 131I for thyroid studies, 67Ga-citrate for infectious workups, and combined stress and rest myocardial perfusion studies, the remainder of the 99mTc-based studies delivered average effective doses clustered below 10 mSv. Between the 2010 survey and the 2014 survey, there was a statistically significant decrease in delivered dose from 18.3 to 14.5 mSv. 67Ga-citrate studies for infectious workups also showed a significant decrease in delivered dose from 31.0 to 26.2 mSv. The standardized CT portion of SPECT/CT studies yielded a mean effective dose 14 times lower than the radiopharmaceutical portion of the study. Conclusion: Between 2010 and 2014, there was a significant decrease in the delivered effective dose in myocardial perfusion and 67Ga-citrate studies. The CT portions of the surveyed SPECT/CT studies contributed a relatively small fraction of the total delivered effective dose.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".