Micro-CT imaging: Developing criteria for examining fetal skeletons in regulatory developmental toxicology studies – A workshop report
Bibliographic record
Abstract
During the past two decades the use and refinements of imaging modalities have markedly increased making it possible to image embryos and fetuses used in pivotal nonclinical studies submitted to regulatory agencies. Implementing these technologies into the Good Laboratory Practice environment requires rigorous testing, validation, and documentation to ensure the reproducibility of data. A workshop on current practices and regulatory requirements was held with the goal of defining minimal criteria for the proper implementation of these technologies and subsequent submission to regulatory agencies. Micro-computed tomography (micro-CT) is especially well suited for high-throughput evaluations, and is gaining popularity to evaluate fetal skeletons to assess the potential developmental toxicity of test agents. This workshop was convened to help scientists in the developmental toxicology field understand and apply micro-CT technology to nonclinical toxicology studies and facilitate the regulatory acceptance of imaging data. Presentations and workshop discussions covered: (1) principles of micro-CT fetal imaging; (2) concordance of findings with conventional skeletal evaluations; and (3) regulatory requirements for validating the system. Establishing these requirements for micro-CT examination can provide a path forward for laboratories considering implementing this technology and provide regulatory agencies with a basis to consider the acceptability of data generated via this technology.
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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 | 0.056 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".