Pathways and challenges in the clinical translational of radiopharmaceuticals for pediatric investigations
Bibliographic record
Abstract
Radiopharmaceutical development and clinical translation face numerous scientific, ethical, and regulatory challenges, particularly within the pediatric population. Although molecular imaging holds significant promise for improving diagnosis and treatment across a spectrum of diseases, including pediatric-specific conditions like Kawasaki disease, autism spectrum disorders, attention-deficit/hyperactivity disorder, and neuroblastoma, the path from discovery to clinical application remains problematic. The U.S. Food and Drug Administration (FDA) provides three primary pathways-traditional Investigational New Drug (IND) applications, exploratory Investigational New Drug application (eIND), and the Radioactive Drug Research Committee (RDRC) mechanism-to facilitate clinical translation of radiotracers. However, these frameworks are not specifically tailored to pediatric needs. Children's heightened sensitivity to ionizing radiation, coupled with physiological variability and ethical concerns, complicates trial design, dosimetry, and informed consent. Current practices also exhibit the limitation of inconsistent dosing standards across institutions. Emerging technologies-including improved single-photon emission computed tomography and positron emission tomography techniques, theranostics, whole-body scanners, and artificial intelligence-driven radiomics-offer potential to reduce these risks by enabling lower doses, reduced scan time, and more precise targeting. Nonetheless, a significant gap remains in translating these innovations into safe, equitable access for pediatric patients. Addressing these challenges requires updated regulatory guidance, ethical frameworks, and robust clinical strategies to ensure equitable access to molecular imaging innovations for children.
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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 | 0.117 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".