Biological Dosimetry by the Triage Dicentric Chromosome Assay: Potential Implications for Treatment of Acute Radiation Syndrome in Radiological Mass Casualties
Bibliographic record
Abstract
Biological dosimetry is an essential tool for estimating radiation dose. The dicentric chromosome assay (DCA) is currently the tool of choice. Because the assay is labor-intensive and time-consuming, strategies are needed to increase throughput for use in radiation mass casualty incidents. One such strategy is to truncate metaphase spread analysis for triage dose estimates by scoring 50 or fewer metaphases, compared to a routine analysis of 500 to 1000 metaphases, and to increase throughput using a large group of scorers in a biodosimetry network. Previously, the National Institutes for Allergies and Infectious Diseases (NIAID) and the Armed Forces Radiobiology Research Institute (AFRRI) sponsored a double-blinded interlaboratory comparison among five established international cytogenetic biodosimetry laboratories to determine the variability in calibration curves and in dose measurements in unknown, irradiated samples. In the present study, we further analyzed the published data from this previous study to investigate how the number of metaphase spreads influences dose prediction accuracy and how this information could be of value in the triage and management of people at risk for the acute radiation syndrome (ARS). Although, as expected, accuracy decreased with lower numbers of metaphase spreads analyzed, predicted doses by the laboratories were in good agreement and were judged to be adequate to guide diagnosis and treatment of ARS. These results demonstrate that for rapid triage, a network of cytogenetic biodosimetry laboratories can accurately assess doses even with a lower number of scored metaphases.
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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".