Bridging the preparedness gap: a systematic review of recommended stockpile items for radiological and nuclear emergencies
Bibliographic record
Abstract
BACKGROUND: While rare, radiological and nuclear (RN) emergencies pose complex challenges that require tailored preparedness strategies. A key aspect is the strategic stockpiling of medical countermeasures (MCMs), yet existing literature offers limited and fragmented recommendations regarding their appropriate composition. METHODS: This systematic review, conducted following PRISMA guidelines, aims to consolidate and critically appraise available evidence on stockpiling for RN emergencies. Seven databases and selected grey literature sources were screened from 2011 onward. Studies were included if they provided direct or indirect recommendations on stockpiling items. Data extraction was conducted in two steps by independent pairs of reviewers, and identified items were categorized as therapeutics, medical devices, personal protective equipment (PPE), or RN-specific equipment. Items were further analyzed for regulatory status, administration route, shelf-life, and storage requirements. RESULTS: Thirty-two articles met the inclusion criteria. Therapeutics dominated the findings, with 50 distinct agents identified, most frequently potassium iodide, Prussian Blue, Ca-/Zn-DTPA, and hematopoietic growth factors such as filgrastim. In contrast, medical devices were not supported by a sufficient level of stockpiling recommendation, while PPE (6 of 32 articles) and RN equipment (14 of 32 articles) were cited less often. Pediatric considerations were rarely addressed, with 2 studies explicitly focused on this population. Gaps in operational guidance, regulatory harmonization, and standardized planning emerged across the literature. CONCLUSIONS: This review highlights that stockpiling recommendations for RN emergencies remain limited, with greater guidance concentrated on a few therapeutics and little direction for other items. The findings provide an informative evidence base to support more consistent policy and preparedness planning.
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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 | 0.025 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".