The capacity, capabilities and needs of the WHO biodosenet member laboratories
Bibliographic record
Abstract
Biodosimetry is an essential tool for providing timely assessments of radiation exposure, particularly when physical dosimetry is unavailable or unreliable. For mass-casualty events involving public exposure to ionising radiation, it is paramount to rapidly provide this dose information for medical management of casualties. The dicentric chromosome assay is currently the most reliable accepted method for biodosimetry; however, in a mass-casualty scenario, the throughput of this assay will be challenged by its time-consuming nature and the specific expertise required. To address this limitation, many countries have established expertise in cytogenetic biodosimetry and started developing surge capabilities through setting up regional networks to deal with emergency situations. To capitalise on this growing expertise and organise it into an internationally coordinated laboratory network, the World Health Organization has created and launched a global biodosimetry network (BioDoseNet). In order to determine the existing capacity of BioDoseNet member laboratories, including their expertise and in vivo experience, involvement in national and international activities, problems, needs and prospects, an in-depth survey was conducted. These survey results provide significant information on the current state of emergency cytogenetic biodosimetry capabilities around the world.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".