GHSI EMERGENCY RADIONUCLIDE BIOASSAY LABORATORY NETWORK – SUMMARY OF THE SECOND EXERCISE
Bibliographic record
Abstract
The Global Health Security Initiative (GHSI) established a laboratory network within the GHSI community to develop collective surge capacity for radionuclide bioassay in response to a radiological or nuclear emergency as a means of enhancing response capability, health outcomes and community resilience. GHSI partners conducted an exercise in collaboration with the WHO Radiation Emergency Medical Preparedness and Assistance Network and the IAEA Response and Assistance Network, to test the participating laboratories (18) for their capabilities in in vitro assay of biological samples, using a urine sample spiked with multiple high-risk radionuclides (90Sr, 106Ru, 137Cs, and 239Pu). Laboratories were required to submit their reports within 72 h following receipt of the sample, using a pre-formatted template, on the procedures, methods and techniques used to identify and quantify the radionuclides in the sample, as well as the bioassay results with a 95% confidence interval. All of the participating laboratories identified and measured all or some of the radionuclides in the sample. However, gaps were identified in both the procedures used to assay multiple radionuclides in one sample, as well as in the methods or techniques used to assay specific radionuclides in urine. Two-third of the participating laboratories had difficulties in determining all the radionuclides in the sample. Results from this exercise indicate that challenges remain with respect to ensuring that results are delivered in a timely, consistent and reliable manner to support medical interventions. Laboratories within the networks are encouraged to work together to develop and maintain collective capabilities and capacity for emergency bioassay, which is an important component of radiation emergency response.
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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 | 0.026 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".