New approaches to historical challenges: Avoiding the early missteps of tobacco research in cannabis studies
Bibliographic record
Abstract
As cannabis use becomes increasingly mainstream for both recreational and medicinal purposes, scientific evaluation of its health effects has not kept pace with legalization and market expansion. This gap echoes historical missteps seen in tobacco regulation, where decades passed before sufficient mechanistic and epidemiologic data on health effects prompted policy action. To avoid repeating such delays in action, this paper advocates for the integration of New Approach Methodologies (NAMs) in cannabis toxicology research, particularly for inhaled products; these tools prioritize human relevance, mechanistic insight, and reduction of animal testing. We highlight three key domains of innovation: (1) air–liquid interface (ALI) exposure systems that more accurately model inhaled cannabis products; (2) human-derived cell models and organoids, including those from induced pluripotent stem cells (iPSCs), which provide insight into tissue-specific toxicity; and (3) computational toxicology platforms such as quantitative structure–activity relationship (QSAR) and physiologically-based pharmacokinetic (PBPK) modeling, which support high-throughput, mechanism-based risk assessment. Together, these tools offer a robust framework for evaluating the diverse and complex constituents of cannabis products, enabling proactive risk assessment and regulation for cannabis-based products.
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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 | 0.296 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.018 | 0.045 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.011 | 0.029 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".