Position paper to facilitate patient access to radiopharmaceuticals: considerations for a suitable pharmaceutical regulatory framework
Bibliographic record
Abstract
BACKGROUND: Nuclear medicine has made enormous progress in the past decades. However, there are still significant inequalities in patient access among different countries, which could be mitigated by improving access to and availability of radiopharmaceuticals. MAIN BODY: This paper summarises major considerations for a suitable pharmaceutical regulatory framework to facilitate patient access to radiopharmaceuticals. These include the distinct characteristics of radiopharmaceuticals which require dedicated regulations, considering the impact of the variable complexity of radiopharmaceutical preparation, personnel requirements, manufacturing practices and quality assurance, regulatory authority interfaces, communication and training, as well as marketing authorisation procedures to ensure availability of radiopharmaceuticals. Finally, domestic and regional supply to ensure patient access via alternative regulatory pathways, including in-house production of radiopharmaceuticals, is described, and an outlook on regulatory challenges faced by new developments, such as the use of alpha emitters, is provided. CONCLUSIONS: All these considerations are an outcome of a dedicated Technical Meeting organised by the IAEA in 2023 and represent the views and opinions of experts in the field, not those of any regulatory authorities.
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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 | 0.030 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.040 | 0.021 |
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".