QUICKSCAN DICENTRIC CHROMOSOME ANALYSIS FOR RADIATION BIODOSIMETRY
Bibliographic record
Abstract
The dicentric chromosome assay (DCA) is the gold-standard assay for accurately estimating unknown radiological doses to individuals following radiological or nuclear accidents. However in a mass-casualty scenario, this assay is not well suited for providing timely dose estimates due to its time- and expertise-intensive nature. In Canada, two approaches are being developed in an attempt to increase triage-quality biological dosimetry throughput. These are 1) increasing the number of trained personnel capable of conducting the DCA, and 2) evaluating alternative biodosimetry approaches or DCA variations. In a recent exercise, a new scoring technique (termed DCA QuickScan) was evaluated as an alternative rapid-scoring approach. Triage-quality conventional DCA and DCA QuickScan analysis were based upon scoring a minimum of 50 metaphase cells or 30 dicentrics by 9-15 scorers across four laboratories. Dose estimates for the conventional DCA were found to be within 0.5 Gy of the actual dose for 83% of the unknown samples, while DCA QuickScan dose estimates were within 0.5 Gy for 80% of the samples. Of the dose estimates falling 0.5 Gy or more outside the actual dose, the majority were dose over-estimates. It was concluded that the DCA QuickScan approach can provide critical dose information at a much faster rate than the conventional DCA without sacrificing accuracy. Future studies will further evaluate the accuracy of the DCA QuickScan method.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".