Biological Dosimetry Intercomparison Exercise: An Evaluation of Triage and Routine Mode Results by Robust Methods
Bibliographic record
Abstract
Well-defined protocols and quality management standards are indispensable for biological dosimetry laboratories. Participation in periodic proficiency testing by interlaboratory comparisons is also required. This harmonization is essential if a cooperative network is used to respond to a mass casualty event. Here we present an international intercomparison based on dicentric chromosome analysis for dose assessment performed in the framework of the IAEA Regional Latin American RLA/9/054 Project. The exercise involved 14 laboratories, 8 from Latin America and 6 from Europe. The performance of each laboratory and the reproducibility of the exercise were evaluated using robust methods described in ISO standards. The study was based on the analysis of slides from samples irradiated with 0.75 (DI) and 2.5 Gy (DII). Laboratories were required to score the frequency of dicentrics and convert them to estimated doses, using their own dose-effect curves, after the analysis of 50 or 100 cells (triage mode) and after conventional scoring of 500 cells or 100 dicentrics. In the conntional scoring, at both doses, all reported frequencies were considered as satisfactory, and two reported doses were considered as questionable. The analysis of the data dispersion among the dicentric frequencies and among doses indicated a better reproducibility for estimated doses (15.6% for DI and 8.8% for DII) than for frequencies (24.4% for DI and 11.4% for DII), expressed by the coefficient of variation. In the two triage modes, although robust analysis classified some reported frequencies or doses as unsatisfactory or questionable, all estimated doses were in agreement with the accepted error of ±0.5 Gy. However, at the DI dose and for 50 scored cells, 5 out of the 14 reported confidence intervals that included zero dose and could be interpreted as false negatives. This improved with 100 cells, where only one confidence interval included zero dose. At the DII dose, all estimations fell within ±0.5 Gy of the reference dose interval. The results obtained in this triage exercise indicated that it is better to report doses than frequencies. Overall, in both triage and conventional scoring modes, the laboratory performances were satisfactory for mutual cooperation purposes. These data reinforce the view that collaborative networking in the case of a mass casualty event can be successful.
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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 | 0.166 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".