Adverse Ocular Impact and Emerging Therapeutic Potential of Cannabis and Cannabinoids: A Narrative Review
Bibliographic record
Abstract
Cannabis is the most used drug worldwide with an estimated 219 million users. This narrative review aims to explore the adverse effects and therapeutic applications of cannabis and cannabinoids on the eye, given its growing clinical and non-clinical uses. The current literature reports several adverse ocular effects of cannabis and cannabinoids, including eyelid tremor, ptosis, reduced corneal endothelial cell density, dry eyes, red eyes, and neuro-retinal dysfunction. Cannabinoids may transiently impair night vision, depth perception, binocular and monocular contrast sensitivity, and dynamic visual acuity. Cannabinoids are not currently considered a first-line treatment option for any ocular conditions. Δ-9-tetrahydrocannabinol been shown to result in short-term intraocular pressure reduction, but insufficient evidence to support its use in treating glaucoma exists. Potential therapeutic applications of cannabinoids include their use as a second-line agent for treatment-refractory blepharospasm, for dry eye disease given corneal anti-inflammatory properties, and for suppression of pendular nystagmus in individuals with multiple sclerosis, which all necessitate further research for informed clinical practices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".