Availability, Quality, and Relevance of Toxicogenomics Data for Human Health Risk Assessment: A Scoping Review of the Literature on Trihalomethanes
Bibliographic record
Abstract
Human health risk assessment (HHRA) must be adapted to the challenges of the 21st century, and the use of toxicogenomics data in HHRA is among the changes that regulatory agencies worldwide are trying to implement. However, the use of toxicogenomics data in HHRA is still limited. The purpose of this study was to explore the availability, quality, and relevance to HHRA of toxicogenomics publications as potential barriers to their use in HHRA. We conducted a scoping review of available toxicogenomics literature, using trihalomethanes as a case study. Four bibliographic databases (including the Comparative Toxicogenomics Database) were assessed. An evaluation table was developed to characterize quality and relevance of studies included on the basis of criteria proposed in the literature. Studies were selected and analyzed by 2 independent reviewers. Only 9 studies, published between 1997 and 2015, were included in the analysis. Based on the selected criteria, critical methodological details were often missing; in fact, only 3 out of 9 studies were considered to be of adequate quality for HHRA. No studies met >3 (out of 7) criteria of relevance to HHRA (eg, adequate number of doses and sample size). This first scoping review of toxicogenomics publications on trihalomethanes shows that low availability, quality, and relevance to HHRA of toxicogenomics publications presents potential barriers to their use in HHRA. Improved reporting of methodological details and study design is needed in the future so that toxicogenomics studies can be appropriately assessed regarding their quality and value for HHRA.
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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 | 0.181 | 0.469 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.049 | 0.042 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".