A quantitative weight-of-evidence review of preclinical studies examining the potential developmental and reproductive toxicity of acetaminophen
Bibliographic record
Abstract
We previously developed a quantitative weight-of-evidence (QWoE) framework using prespecified scoring criteria for preclinical acetaminophen data to characterize potential developmental neurotoxicity outcomes with considerations for biological relevance of the response to adverse outcomes and the strength of methods and study design. The current analysis uses this framework to characterize potential developmental and reproductive toxicity (DART) outcomes following exposure to acetaminophen. Two-hundred forty-two QWoE entries were documented from in vivo rodent studies identified in 110 publications across five categories: DART endpoints in the context of (1) periadolescent/adulthood (nonpregnancy) exposures; (2) pregnant female exposures; and, for in utero or other developmental exposures, (3) anatomical abnormalities, (4) reproductive development, and (5) other physical development. A mean outcome score and methods score were calculated for 242 QWoE entries. Data analyzed in our framework were of moderate quality showing no consistent evidence of DART in male and female rodents following exposure to acetaminophen at therapeutic and/or non-systemically toxic doses. Similar results were found for the individual context- and outcome-related endpoint analyses and as segregated by sex. Overall, this QWoE analysis on the in vivo rodent data demonstrated no consistent evidence of adverse effects following exposure to therapeutic and/or non-systemically toxic acetaminophen on development or on the structure and function of the reproductive system.
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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 | 0.044 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.041 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".