Laboratory Intercomparison of the Dicentric Chromosome Analysis Assay
Bibliographic record
Abstract
The study design and obtained results represent an intercomparison of various laboratories performing dose assessment using the dicentric chromosome analysis (DCA) as a diagnostic triage tool for individual radiation dose assessment. Homogenously X-irradiated (240 kVp, 1 Gy/min) blood samples for establishing calibration data (0.25-5 Gy) as well as blind samples (0.1-6.4 Gy) were sent to the participants. DCA was performed according to established protocols. The time taken to report dose estimates was documented for each laboratory. Additional information concerning laboratory organization/characteristics as well as assay performance was collected. The mean absolute difference (MAD) was calculated and radiation doses were merged into four triage categories reflecting clinical aspects to calculate accuracy, sensitivity and specificity. The earliest report time was 2.4 days after sample arrival. DCA dose estimates were reported with high and comparable accuracy, with MAD values ranging between 0.16-0.5 Gy for both manual and automated scoring. No significant differences were found for dose estimates based either on 20, 30, 40 or 50 cells, suggesting that the scored number of cells can be reduced from 50 to 20 without loss of precision of triage dose estimates, at least for homogenous exposure scenarios. Triage categories of clinical significance could be discriminated efficiently using both scoring procedures.
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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 | 0.031 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".