Intercomparison of Conventional and QuickScan Dicentric Scoring for the Validation of Individual Biodosimetry Analysis in Taiwan
Bibliographic record
Abstract
The dicentric chromosome assay (DCA), the gold standard for radiation biodosimetry, evaluates an individual absorbed radiation dose by the analysis of DNA damage in human lymphocytes. The conventional (C-DCA) and QuickScan (QS-DCA) scoring methods are sensitive for estimating radiation dose. The Biodosimetry Laboratory at Institute of Nuclear Energy Research (INER), Taiwan, participated in intercomparison exercises conducted by Health Canada (HC) in 2014, 2015 and 2018 to validate the laboratory’s accuracy and performance. Blood samples for the conventional dose response curve for Taiwan were irradiated with 0, 0.25, 0.5, 1, 2, 3, 4 and 5 Gy. Ten blind blood samples were provided by HC. Either or both of two methods of conventional (C) or QuickScan (QS) scoring could be chosen for the HC’s intercomparison. For C-DCA triage scoring, only cells with 46 centromeres were counted and each scorer recorded the number of dicentrics in the first 50 metaphases or stopped scoring when 30 dicentrics were reached. Scorers also recorded how much time it took to analyze 10, 20, and 50 cells. Subsequently, the data were entered into the Dose Estimate software (DoseEstimate_v5.1) and dose estimates were calculated. With QS-DCA scoring, a minimum of 50 metaphase cells (or 30 dicentrics) were scored in apparently complete metaphases without verification of exactly 46 centromeres. For the blinded blood samples irradiated at HC and shipped to INER, the mean absolute deviation (MAD) derived after scoring 50 cells for C-DCA and QS-DCA was <0.5 Gy for all three intercomparisons, meeting the criteria for acceptance. The results indicated that the Biodosimetry Laboratory at INER can provide reliable dose estimates in the case of a large-scale radiation accident.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".