The iCannTookit: a consensus‐based, flexible framework for measuring contemporary cannabis use
Bibliographic record
Abstract
We welcome the commentary by Reddy [1], who outlines that the toolkit is a promising step towards standardizing the quantification of cannabis exposure. We acknowledge that the field of cannabis research has been historically characterized by a lack of consensus about what a standard unit of cannabis is. However, in recent years there has been considerable progress in this area. A proposal for a 5-mg standard tetrahydrocannabinol (THC) unit [2, 3] was subjected to an extensive information-gathering process by the US National Institute on Drug Abuse (NIDA), including input from international researchers, stakeholders and members of the public (Notice number: NOT-DA-20-043). Overall, there was support for the idea and this has now been endorsed by NIDA (https://nida.nih.gov/about-nida/noras-blog/2021/05/establishing-5mg-thc-standard-unit-research) [4], National Heart, Lung and Blood Institute (NHLBI), National Institute of Mental Health (NIMH) and National Cancer Institute (NCI) of the National Institute of Health (NIH), American Psychological Association and College on Problems of Drug Dependence, with guidelines for reporting now available [5]. In summary, there is emerging consensus for a 5-mg standard THC unit. The iCannToolkit can be used to estimate the 5-mg standard THC unit, but it is a flexible tool and, if necessary, therefore can be used to estimate alternative measures such as the standard joint unit [6, 7]. To date, biological measures of the top layer of the toolkit can objectively measure the Standard THC Unit for estimating THC exposure. Biological measures can measure THC exposure at a given point in time and should be integrated with self-report data, because chronicity of use impacts on cannabinoid metabolites. The toolkit allows researchers and clinicians flexibility to measure self-report data (layers 1 and 2) independently or in addition to biological measures. Such flexibility enables measuring aspects of cannabis exposure (e.g. potency, age of onset, duration of use, method of use) in order to understand cannabis use-related health risks and benefits. The iCannToolkit has been created to objectively measure cannabis exposure, including medicinal and non-medicinal uses, in keeping with increasing global trends for decriminalization of recreational and medicinal cannabis use. We hope that future studies will use the toolkit alongside measures of cannabis related benefits and harms, to generate high-quality evidence and advance the field. This study was funded by the Society for the Study of Addiction, European Monitoring Centre for Drugs and Drug Addiction. H.L.P. works under the CERCA Programme/Generalitat de Catalunya and receives funding from the Spanish Ministry of Science, Innovation and Universities, Instituto de Salud Carlos III through a ‘Juan Rodes’ contract (JR19/00025). A.G. received funding from Novartis for work outside this area (a Phase III cocaine trial). A.E. has received a speaker honorarium from GW Pharmaceuticals. A.W. is the founder of the Global Drug Survey. C.H. became a full-time employee of GW Pharmaceuticals after the consensus meeting. D.H. has served as a paid expert witness on behalf of public health authorities in Canada in response to legal challenges from the cannabis industry. H.L. has received honoraria and travel grants from Janssen and Lundbeck. J.B. has received unrestricted research funding to study smoking cessation from companies who manufacture smoking cessation medications (Pfizer and J&J). R.V. receives consulting fees for Canopy Health Innovations and Syqe Medical Ltd, and is on the Scientific Advisory Board for MyMD Pharmaceuticals and Artiam Bio Inc. V.C. has consulted for Janssen. V.L., W.H., T.P.F., E.W., T.G., W.L., A.C.C., J.P.C., R.L.P., M.v.L., K.P., P.G., M.A.E., S.H.G., J.M. and C.M. have no competing interests to declare.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".