MétaCan
Menu
Back to cohort
Record W1809992158 · doi:10.1111/add.13155

Confirmation of the trials and tribulations of vaping

2015· letter· en· W1809992158 on OpenAlexaboutno aff
Alan J. Budney, James D. Sargent, Dustin C. Lee

Bibliographic record

VenueAddiction · 2015
Typeletter
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institute on Drug Abuse
KeywordsCannabisRegulatory scienceAction (physics)Public healthScientific evidenceMedicineMEDLINEPolitical sciencePublic relationsEnvironmental healthPublic economicsPsychiatryLawEconomicsNursing

Abstract

fetched live from OpenAlex

Responses to our article indicate consensus on the need for expedited scientific and regulatory action related to vaping of cannabis and other substances to curtail untoward public health impact and identify potential benefits. How to speed up science, increase knowledge and enact responsible regulatory policy poses a formidable challenge. The responses to our paper on the emerging phenomenon of vaping cannabis confirmed the pressing need to understand more clearly its public health impact, but this should not keep us from implementing common-sense policies before all the data can be gathered. Dr Tashkin, the foremost expert on the toxicology of cannabis smoke, suggests that the health benefit of vaping cannabis is probably limited to a reduction in symptoms of bronchitis 1. This may benefit cannabis users with compromised lung health, but overall produces fewer health benefits than those projected from using e-cigs to replace burning tobacco. Fischer and colleagues speculate that proliferation of vaping devices could herald the development of novel and more hazardous cannabis formulations for use in these devices 2. They reflect on how vaping may decrease the perceived risk associated with cannabis use, and thereby increase use and the negative consequences of misuse. Drawing from observations from Canada they warn that, as with e-cigs, consumers appear to be ahead of policy leaders, underscoring the urgent need to gain control over already well-established operations of the cannabis and vaping industries. One example of how to combat industry is the State of California's Blue Ribbon Commission on Marijuana Regulation, which is adapting tobacco policies previously implemented successfully to control tobacco proliferation 3. Dr Cox focuses on the need for increased regulation of the commercialization of vaporisers to minimize the adverse impact of vaping 4. He reminds us of the tobacco industry's huge commitment to developing vaping techniques, and long-standing interest in cannabis products, confirmed by a study of tobacco industry documents 5. Dr Cox calls for a coordination of global research efforts on vaping of tobacco, cannabis and other substances to fill critical knowledge gaps and inform policy before Industry can exploit our ignorance. To this end, some aspects of good policy based on tobacco control, for example limits on the ability to market to teens, need not wait for new data. Dr Gartner provides an Australian perspective that reminds us how difficult it can be to get policy right 6. She educates the reader about ‘mulling’, the practice of mixing tobacco with cannabis to assist with the burning cannabis and provide a smoother taste. Mulling may increase the prevalence of tobacco use by serving as a gateway for tobacco initiation. Gartner suggests that vaping may reduce mulling which may, in turn, reduce tobacco use. Her point that we need to consider carefully the possible benefits of vaping alongside potential concerns is well taken. Generally, most commentators agreed that the scientific and regulatory communities must ramp up efforts related to the vaping of cannabis, tobacco and even other substances, such as caffeine 7. As the availability and popularity of vaping rockets ahead, many marketers will capitalize on its profitability with little regard for public health implications. Writing commentaries such as ours about this issue is easy; the more formidable challenge is how to speed up science, increase knowledge and get responsible regulatory policies enacted.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.074
GPT teacher head0.348
Teacher spread0.274 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations7
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAddictionSame topicCannabis and Cannabinoid ResearchFrench-language works237,207