Assessment of patients hospitalized in the Clinical Toxicology and Cardiology Department in Lublin due to poisoning with Cannabis derivatives as a voice in the discussion on the legalization of marijuana
Bibliographic record
Abstract
The article aims to discuss the justification for the legalization of cannabinoids based on the analysis of data from countries where marijuana has been legalized for both medical and recreational use, as well as a retrospective analysis of the medical documentation of patients from the Clinical Toxicology and Cardiology Department and Internal Diseases of the Provincial Specialist Hospital named after Stefan Cardinal Wyszyński in Lublin (years 2020-2024). During the analysis of hospital documentation, a slight upward trend in the number of hospitalizations due to cannabinoid poisoning was observed over the years 2020-2024. In 2024, 54 cases of hospitalization due to poisoning with Cannabis derivatives were recorded, with an average length of hospitalization of 3 days. The vast majority of patients were men (83%) under the age of 26. In 78% of patients, co-occurrence of poisoning with other psychoactive substances was noted, with the most common being alcohol (29 cases), LSD (12 cases), and benzodiazepines (11 cases). 44 patients required consultation with a psychologist or psychiatrist. Suspected suicide attempts were noted in 12 patients. In 38 patients, psychomotor agitation occurred, with pharmacological sedation required in 34 patients. In 22 cases, there was a significant increase in creatine levels, which may indicate an increased risk of rhabdomyolysis. Press analysis showed that the legalization of marijuana does not lead to immediate and significant changes in public health indicators, except for issues related to road safety. Shortly after the legalization of recreational marijuana in some U.S. states and Canada, an increase in consultations with doctors was observed, caused by both acute and chronic effects of cannabinoid poisoning. Due to the short time since marijuana was legalized, research and statistical analyses are still incomplete and often lead to various, contradictory conclusions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".