Interlaboratory Comparison of the Dicentric Chromosome Assay for Radiation Biodosimetry in Mass Casualty Events
Bibliographic record
Abstract
This interlaboratory comparison validates the dicentric chromosome assay for assessing radiation dose in mass casualty accidents and identifies the advantages and limitations of an international biodosimetry network. The assay's validity and accuracy were determined among five laboratories following the International Organization for Standardization guidelines. Blood samples irradiated at the Armed Forces Radiobiology Research Institute were shipped to all laboratories, which constructed individual radiation calibration curves and assessed the dose to dose-blinded samples. Each laboratory constructed a dose-effect calibration curve for the yield of dicentrics for (60)Co gamma rays in the 0 to 5-Gy range, using the maximum likelihood linear-quadratic model, Y = c + alphaD + betaD(2). For all laboratories, the estimated coefficients of the fitted curves were within the 99.7% confidence intervals (CIs), but the observed dicentric yields differed. When each laboratory assessed radiation doses to four dose-blinded blood samples by comparing the observed dicentric yield with the laboratory's own calibration curve, the estimates were accurate in all laboratories at all doses. For all laboratories, actual doses were within the 99.75% CI for the assessed dose. Across the dose range, the error in the estimated doses, compared to the physical doses, ranged from 15% underestimation to 15% overestimation.
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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 | 0.053 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".