Part 1: Evaluation of Pediatric Cannabis–Drug Interaction Reports
Bibliographic record
Abstract
Data addressing safety concerns related to potential drug interactions between cannabis-derived products and pharmaceutical medications in the pediatric population are lacking. In this study, we retrieved case reports through a published literature search using PubMed and spontaneous reporting data using the Food and Drug Administration's Adverse Event Reporting System (FAERS) to identify potential cannabis- and cannabinoid-drug interactions in individuals younger than 18 years old. To evaluate the published case reports, we used the Drug Interaction Probability Scale (DIPS), a 10-item questionnaire designed to discern the causal relationship between a potential drug interaction and adverse drug reactions (ADRs). FAERS reports were deduplicated and analyzed to gather information regarding patient demographics, associated drugs, nature of the ADRs, outcomes, professions of the reporters, and reporting timelines. Seven published case reports and 9142 FAERS ADRs reports were included in the final analysis. Based on the findings, caution is warranted when cannabis or cannabinoids are used in combination with prescribed medications, including methadone, everolimus, fluoxetine, and paroxetine. Cannabinoids may inhibit drug-metabolizing enzymes, including several cytochrome P450s, leading to increased drug exposure and potentially, an increased risk for ADRs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".