Interlaboratory comparison of dicentric chromosome assay using electronically transmitted images
Bibliographic record
Abstract
The bottleneck in data acquisition during biological dosimetry based on a dicentric assay is the need to score dicentrics in a large number of lymphocytes. One way to increase the capacity of a given laboratory is to use the ability of skilled operators from other laboratories. This can be done using image analysis systems and distributing images all around the world. Two exercises were conducted to test the efficiency of such an approach involving 10 laboratories. During the first exercise (E1), the participant laboratories analysed the same images derived from cells exposed to 0.5 and 3 Gy; 100 images were sent to all participants for both doses. Whatever the dose, only about half of the cells were complete with well-spread metaphases suitable for analysis. A coefficient of variation (CV) on the standard deviation of ∼15 % was obtained for both doses. The trueness was better for 3 Gy (0.6 %) than for 0.5 Gy (37.8 %). The number of estimated doses classified as satisfactory according to the z-score was 3 at 0.5 Gy and 8 at 3 Gy for 10 dose estimations. In the second exercise, an emergency situation was tested, each laboratory was required to score a different set of 50 images in 2 d extracted from 500 downloaded images derived from cells exposed to 0.5 Gy. Then the remaining 450 images had to be scored within a week. Using 50 different images, the CV on the estimated doses (79.2 %) was not as good as in E1, probably associated to a lower number of cells analysed (50 vs. 100) or from the fact that laboratories analysed a different set of images. The trueness for the dose was better after scoring 500 cells (22.5 %) than after 50 cells (26.8 %). For the 10 dose estimations, the number of doses classified as satisfactory according to the z-score was 9, for both 50 and 500 cells. Overall, the results obtained support the feasibility of networking using electronically transmitted images. However, before its implementation some issues should be elucidated, such as the number and resolution of the images to be sent, and the harmonisation of the scoring criteria. Additionally, a global website able to be used for the different regional networks, like Share Points, will be desirable to facilitate worldwide communication.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".