EURADOS/REMPAN Review on Monitoring and Dosimetry for Radionuclide-contaminated Wounds
Bibliographic record
Abstract
The European Radiation Dosimetry Group (EURADOS) and the WHO's Radiation Emergency Medical Preparedness and Assistance Network (REMPAN) have collaborated to review best practices for managing radionuclide intakes through wounds. Rapid response and decisions on wound decontamination, tissue excision, and chelation therapy are based on measurements of the exposed individual and preliminary dose assessments using reasonable default assumptions. The goal is to minimize exposure, prevent tissue reactions, and reduce the risk of stochastic effects.The management of a contaminated wound is always case-specific, but some general procedures typically apply for a proper evaluation of the contamination case. Medical doctors (surgeons and toxicologists) and internal dosimetrists should work together in the management of the contaminated wound case, with internal dosimetrists providing expert advice to aid clinical decision-making and communication with the patient and his/her family. The ISO standard 20031:2020 provides guidelines on the monitoring and dosimetry for internal exposures due to wound contamination with radionuclides. The Clinical Decision Guide was proposed by the National Council on Radiation Protection and Measurements in its Report 161 to assist physicians in making treatment decisions for individuals with internal radionuclide intakes. Best practices for medical treatment, based on previous experience, are presented here.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".