Transformer-Based Topic Modeling: Characterizing Cannabis Product Adverse Experiences Self-Reported as Requiring Medical Attention on Reddit.

Tim Ken Mackey, Matthew C Nali, Meng Zhen Larsen, Zhuoran Li, Cassandra L Taylor, Beverly Wolpert, Catharine Trice

Abstract read

In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Tim Ken MackeyGlobal Health Program, Department of Anthropology, University of California, San Diego, La Jolla, CA, United States.ORCID https://orcid.org/0000-0002-2191-7833

Matthew C NaliSan Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, United States.ORCID https://orcid.org/0000-0002-0565-8202

Meng Zhen LarsenSan Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, United States.ORCID https://orcid.org/0009-0002-8305-0718

Zhuoran LiS-3 Research LLC, San Diego, CA, United States.ORCID https://orcid.org/0000-0001-6192-4301

Cassandra L TaylorCenter for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States.ORCID https://orcid.org/0000-0001-7476-2049

Beverly WolpertOffice of Surveillance Strategy and Risk Prioritization, Human Foods Program, U.S. Food and Drug Administration, College Park, MD, United States.ORCID https://orcid.org/0000-0002-3083-7384

Catharine TriceOffice of Surveillance Strategy and Risk Prioritization, Human Foods Program, U.S. Food and Drug Administration, College Park, MD, United States.ORCID https://orcid.org/0009-0002-3800-8502

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study uses keyword filtering, a transformer-based algorithm, and inductive content coding to identify and characterize cannabis adverse experiences as discussed on the social media platform Reddit and reports a total of 1177 self-reported adverse experiences requiring medical attention.

Indexed as

CannabisSelf ReportSocial MediaAlgorithmsHumansMachine Learningadverse eventscannabismachine learning, topic modelingreddit

Identifiers

PMID41637739

DOI10.2196/82661

PMCPMC12917483

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.