Dealing with at-risk populations in radiological/nuclear emergencies
Bibliographic record
Abstract
In a mass casualty event, there will be at-risk populations that will require unique triage, treatment and consequence management to minimise immediate and long-term health effects. This statement is particularly true for radiological/nuclear (R/N) disasters where individuals exhibit a broad range of physiological responses to radiation exposure. For example, immunocompromised individuals will experience more detrimental radiation health effects; however, it is not always possible to definitively identify these individuals at the time of triage. Immediate and long-term consequence management for these individuals may require unique and potentially limited resources. Thus, at the time of an R/N event, it is crucial to assist community planners by: (a) rapidly identifying at-risk individuals who may have been exposed; (b) determining the dose and individual-specific health risks associated with radiation exposure; (c) identifying additional resources needed to deal with unique, population-specific requirements; and (d) developing treatment strategies in keeping with the rules of 'supply and demand'. A comprehensive approach to identifying issues relevant to the R/N emergency preparedness for dealing with at-risk populations will be discussed with the aim of defining future research objectives.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".