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Record W4405766414 · doi:10.1002/prp2.70047

Part 2: Drug Interactions Involving Cannabis Products in Persons Aged 18 and Over: A Summary of Published Case Reports and Analysis of the <scp>FDA</scp> Adverse Event Reporting System

2024· review· en· W4405766414 on OpenAlexaff
Maryann R. Chapin, Sandra L. Kane‐Gill, Xiaotong Li, Kojo Abanyie, Sanya B. Taneja, Susan Egbert, Mary F. Paine, Richard D. Boyce

Bibliographic record

VenuePharmacology Research & Perspectives · 2024
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Manitoba
FundersU.S. National Library of MedicineNational Institutes of HealthNational Center for Complementary and Integrative HealthUniversity of PittsburghOffice of Dietary Supplements
KeywordsCannabisAdverse Event Reporting SystemDrugMedicineAdverse effectMedical prescriptionCausality (physics)PsychiatryPharmacology

Abstract

fetched live from OpenAlex

The increasing utilization of cannabis products combined with lack of data regarding potential cannabis-prescription drug interactions is concerning. This study aimed to review published case reports and FDA Adverse Event Reporting System (FAERS) spontaneous reports to assess cannabis-drug interactions in persons aged 18 and over. A literature search identified 20 case reports that were each assessed for drug interaction causality using the Drug Interaction Probability Scale. Data collected from the FAERS revealed a greater proportion of reports mentioning serious outcomes, including death, when cannabis was used concomitantly with controlled substances compared to noncontrolled substances. Fisher's exact test showed a statistically significant difference between the controlled and noncontrolled groups (p = 0.043). Overall, these findings emphasize the need for additional research and vigilant monitoring of cannabis use when combined with other medications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.447
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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