Animal Models for Medical Countermeasures to Radiation Exposure
Bibliographic record
Abstract
Since September 11, 2001, there has been the recognition of a plausible threat from acts of terrorism, including radiological or nuclear attacks. A network of Centers for Medical Countermeasures against Radiation (CMCRs) has been established across the U.S.; one of the missions of this network is to identify and develop mitigating agents that can be used to treat the civilian population after a radiological event. The development of such agents requires comparison of data from many sources and accumulation of information consistent with the "Animal Rule" from the Food and Drug Administration (FDA). Given the necessity for a consensus on appropriate animal model use across the network to allow for comparative studies to be performed across institutions, and to identify pivotal studies and facilitate FDA approval, in early 2008, investigators from each of the CMCRs organized and met for an Animal Models Workshop. Working groups deliberated and discussed the wide range of animal models available for assessing agent efficacy in a number of relevant tissues and organs, including the immune and hematopoietic systems, gastrointestinal tract, lung, kidney and skin. Discussions covered the most appropriate species and strains available as well as other factors that may affect differential findings between groups and institutions. This report provides the workshop findings.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".