Bibliographic record
Abstract
With the recent legalization of recreational cannabis in Canada and 11 states of the U.S., the interest surrounding cannabis use is increasing. However, many people and even clinicians in Korea do not have exact knowledge about the psychiatric consequences of cannabis use. In this narrative review, the characteristics of cannabis, the endocannabinoid system, and the psychiatric consequences of cannabis use were provided. Cannabis contains more than 80 cannabinoids in the native plant. Psychotropic properties of Δ-9-tetrahydrocannabinol and cannabidiol are most well studied. The two main receptors are cannabinoid-1 receptor and cannabinoid-2 receptor. Several endocannabinoids, such as anandamide and 2-arachidonoylglycerol, act on the receptors as the endogenous ligands. Cannabis influences mood, cognitive functions, and psychomotor functions in acute phase responses, increasing the odds ratio for motor vehicle crashes. Long-term cannabis use is associated with various psychotic outcomes, including the development of schizophrenia, although there is interindividual variability. Cannabis adversely influences learning, memory, and attention. More frequent, persistent, and earlier onset cannabis use is associated with greater cognitive impairment. The chronic cognitive effects of cannabis are complex and controversial. Cannabis has addictive potential, and cannabis use disorder is common. Clinicians should have evidence-based knowledge about the consequences of cannabis use and communicate accurate information about cannabis use and its associated risks to the public.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.024 |
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".