Changing Events and Altitudes on Cannabis
Bibliographic record
Abstract
Dear Editor, We have keenly considered implications of the article by Khadanga et al.[1] published in this journal’s June edition, against the background of changing events and attitudes on cannabis. News reports on India’s first Cannabis Research Project follow hot on the heels of this article, describing the collaboration of CSIR-Indian Institute of Integrative Medicine, Jammu, with medical cannabis research company IndusCann (Toronto, Canada) aimed at developing therapeutics for neuropathies and cancer among others.[2] Soon, Himachal Pradesh became the third state to legalize cannabis cultivation, bringing the clandestine but widespread “weed haven” reputation to “legal light” in India.[3] The subject of undue restrictions on research of cannabis owing to its unscientific classification as a Schedule I drug deserves a separate discussion. However, we call attention to Gendy et al.’s[4] survey aimed at discriminating the prevalence of cannabis use disorder (CUD) between cohorts using cannabis for medical purposes versus those using it for recreational purposes also. Adopting a standard questionnaire-based method of classifying patients admitted to an addiction medicine service in Ontario, the group discovered a staggering 23.1% difference in the prevalence of dual-use patients found fitting the criteria for CUD compared to medical-use-only patients, with concurrently high measures of CUD severity. Khadanga et al.’s[1] report share some resemblance with the above Canadian paper in ways including participant selection, use of psychiatric evaluation methods, and even the period of study (2019). It nevertheless stands out by choosing to study caregiver burden on partners rather than the health of actual patients of substance use disorder. It is already a concern to see almost 12% of participants in this study reporting cannabis use in a country that has not yet legalized it. A number of reviews have found an increase in cannabis prevalence post-legalization, though there exist others that found neutral results.[5] A news article on the Columbia University’s Department of Psychiatry website sought comments from the lead author of a study, who expressed surprise at finding how adversely cannabis affects teenagers who do not even fall in the criteria of CUD.[6] This study, which compared the health effects of cannabis on CUD-positive and CUD-negative teenagers, hints at legalization affecting the change in the perception of cannabis as benign.[7] We wish to add that misinformation regarding the therapeutic effects of cannabinoids (not cannabis) is an equally important factor, both intrinsically and due to its potential for contributing to commercialization of cannabis. Does a solution exist? Unlike Canada and the United States, India certainly does not seem to be down the path of treating cannabis on the same level as intoxicants and narcotics. But neither can one genuinely hope for an early paradigm shift? Instead, it regards the latter to be as much of a malpractice as cannabis or opioids. Undoubtedly, research facilitation for plant-derived cannabinoids having therapeutic value is a must. But the essential task of revamping education and public awareness on the reality of cannabis goes hand in glove with development and promotion of medicines derived from the plant, and all these need to be acted upon together. Data availability statement Not Applicable. Author’s contribution RM: Design, literature search, manuscript preparation; JB: Manuscript review, guidance; ST: Manuscript review, guidance; PKK: Concept design, literature search, manuscript editing/review, guidance, correspondence. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".