Operational Testing of a Combined Hardware-Software Strategy for Triage of Radiologically-Contaminated Persons
Bibliographic record
Abstract
After a radiological dispersal device (RDD) event, it is possible for radionuclides to enter the human body through inhalation, ingestion, and skin and wound absorption. The dominant pathway will be through inhalation. From a health physics perspective, it is important to know the magnitude of the intake to perform dosimetric assessments. From a medical perspective, removal of radionuclides leading to dose (hence risk) aversion is of high importance. The efficacy of medical decorporation strategies is extremely dependent upon the time of treatment delivery after intake. The "golden hour," or more realistically 3-4 h, is imperative when attempting to increase removal of radionuclides from extracellular fluids prior to cellular incorporation. To assist medical first response personnel in making timely decisions regarding appropriate treatment delivery modes, a software tool has been developed which compiles existing radionuclide decorporation therapy data and allows a user to perform simple triage leading to potential appropriate decorporation treatment strategies. Three triage algorithms were included: (1) multi-parameter model (MPM), (2) clinical decision guidance (CDG) model, and (3) annual limit on intake (ALI) model. A radiation triage mask (RTM) has simultaneously been developed to provide a simple and rapid hardware solution for first responders to triage internally exposed personnel in the field. The hardware/software strategy was field tested with a military medical unit and was found by end-users to be relatively simple to learn and use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".