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Record W4388508399 · doi:10.2105/ajph.2023.307414

Health Effects of High-Concentration Cannabis Products: Scoping Review and Evidence Map

2023· article· en· W4388508399 on OpenAlexfundno aff
Lisa Bero, Rosa Lawrence, Jean-Pierre Oberste, Tianjing Li, Louis Leslie, Thanitsara Rittiphairoj, Christi Piper, George Sam Wang, Ashley Brooks‐Russell, Tsz Wing Yim, Gregory Tung, Jonathan M. Samet

Bibliographic record

VenueAmerican Journal of Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
FundersHealth CanadaColorado Department of Public Health and EnvironmentColorado State University
KeywordsCannabisHuman healthMedicineProduct (mathematics)Effects of cannabisMEDLINEEnvironmental healthPsychologyPsychiatryBiology

Abstract

fetched live from OpenAlex

Background. The concentration of pharmacologically active tetrahydrocannabinol (THC) in cannabis products has been increasing over the past decade. Concerns about potential harmful health effects of using these increasingly higher-concentration products have led some states to consider regulation of cannabis product THC concentration. We conducted a scoping review of health effects of high-concentration cannabis products to inform policy on whether the THC concentrations of cannabis product should be regulated or limited. Objectives. We conducted a scoping review to (1) identify and describe human studies that explore the relationship of high-concentration cannabis products with any health outcomes in the literature and (2) create an interactive evidence map of the included studies to facilitate further analyses. Search Methods. An experienced medical information specialist designed a comprehensive search strategy of 7 electronic databases. Selection Criteria. We included human studies of any epidemiological design with no restrictions by age, sex, health status, country, or outcome measured that reported THC concentration or included a known high-concentration cannabis product. Data Collection and Analysis. We imported search results into Distiller SR, and trained coders conducted artificial intelligence‒assisted screening. We developed, piloted, and revised data abstraction forms. One person performed data abstraction, and a senior reviewer verified a subset. We provide a tabular description of study characteristics, including exposures and outcomes measured, for each included study. We interrogated the evidence map published in Tableau to answer specific questions and provide the results as text and visual displays. Main Results. We included 452 studies in the scoping review and evidence map. There was incomplete reporting of exposure characteristics including THC concentration, duration and frequency of use, and products used. The evidence map shows considerable heterogeneity among studies in exposures, outcomes, and populations studied. A limited number of reports provided data that would facilitate further quantitative synthesis of the results across studies. Conclusions. This scoping review and evidence map support strong conclusions concerning the utility of the literature for characterizing risks and benefits of the current cannabis marketplace and the research approaches followed in the studies identified. Relevance of the studies to today’s products is limited. Public Health Implications. High-quality evidence to address the policy question of whether the THC concentration of cannabis products should be regulated is scarce. The publicly available interactive evidence map is a timely resource for other entities concerned with burgeoning access to high-concentration cannabis. (Am J Public Health. 2023;113(12):1332–1342. https://doi.org/10.2105/AJPH.2023.307414 )

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.169
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.061
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.169
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0610.051
Science and technology studies0.0020.002
Scholarly communication0.0080.007
Open science0.0040.005
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.048
GPT teacher head0.396
Teacher spread0.348 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations14
Published2023
Admission routes1
Has abstractyes

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