Cannabis‐related allergies: An international overview and consensus recommendations
Bibliographic record
Abstract
Cannabis is the most widely used recreational drug in the world. Cannabis sativa and Cannabis indica have been selectively bred to develop their psychoactive properties. The increasing use in many countries has been accelerated by the COVID-19 pandemic. Cannabis can provoke both type 1 and type 4 allergic reactions. Officially recognized allergens include a pathogenesis-related class 10 allergen, profilin, and a nonspecific lipid transfer protein. Other allergens may also be relevant, and recognition of allergens may vary between countries and continents. Cannabis also has the potential to provoke allergic cross-reactions to plant foods. Since cannabis is an illegal substance in many countries, research has been hampered, leading to challenges in diagnosis since no commercial extracts are available for testing. Even in countries such as Canada, where cannabis is legalized, diagnosis may rely solely on the purchase of cannabis for prick-to-prick skin tests. Management consists of avoidance, with legal issues hindering the development of other treatments such as immunotherapy. Education of healthcare professionals is similarly lacking. This review aimed to summarize the current status of cannabis allergy and proposes recommendations for the future management of this global issue.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".