The Internet’s Role in a Biodosimetric Response to a Radiation Mass Casualty Event
Bibliographic record
Abstract
Response to a large-scale radiological incident could require timely medical interventions to minimize radiation casualties. Proper medical care requires knowing the victim's radiation dose. When physical dosimetry is absent, radiation-specific chromosome aberration analysis can serve to estimate the absorbed dose in order to assist physicians in the medical management of radiation injuries. A mock exercise scenario was presented to six participating biodosimetry laboratories as one individual acutely exposed to Co under conditions suggesting whole-body exposure. The individual was not wearing a dosimeter and within 2-3 h of the incident began vomiting. The individual also had other medical symptoms indicating likelihood of a significant dose. Physicians managing the patient requested a dose estimate in order to develop a treatment plan. Participating laboratories in North and South America, Europe, and Asia were asked to evaluate more than 800 electronic images of metaphase cells from the patient to determine the dicentric yield and calculate a dose estimate with 95% confidence limits. All participants were blind to the physical dose until after submitting their estimates based on the dicentric chromosome assay (DCA). The exercise was successful since the mean biological dose estimate was 1.89 Gy whereas the actual physical dose was 2 Gy. This is well within the requirements for guidance of medical management. The exercise demonstrated that the most labor-intensive step in the entire process (visual evaluation of images) can be accelerated by taking advantage of world-wide expertise available on the Internet.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".