Cannabis and psychosis: Neurobiology
Bibliographic record
Abstract
Cannabis is a known risk factor for schizophrenia, although the exact neurobiological process through which the effects on psychosis occur is not well-understood. In this review, we attempt to develop and discuss a possible pathway for the development of psychosis. We examine the neurobiological changes due to cannabis to see if these changes are similar to those seen in schizophrenic patients the findings show similarities; however, these mere similarities cannot establish a 'cause-effect' relationship as a number of people with similar changes do not develop schizophrenia. Therefore, the 'transition-to-psychosis' due to cannabis, despite being a strong risk factor, remains uncertain based upon neurobiological changes. It appears that other multiple factors might be involved in these processes which are beyond neurobiological factors. Major advances have been made in understanding the underpinning of marijuana dependence, and the role of the cannabinoid system, which is a major area for targeting medications to treat marijuana withdrawal and dependence, as well as other addictions is of now, it is clear that some of the similarities in the neurobiology of cannabis and schizophrenia may indicate a mechanism for the development of psychosis, but its trajectories are undetermined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".