Cannabis and glaucoma: A literature review
Bibliographic record
Abstract
Introduction: Primary open-angle glaucoma (POAG) is characterized by the loss of retinal ganglion cells secondary to optic neuropathy; increased intraocular pressure (IOP) may or may not be present. Many treatment options focus on decreasing IOP measurements to attempt to prevent progression of glaucoma. Our literature review addressed a relatively common question; if cannabis is effective for treating elevated IOP in patients with glaucoma. Objective: To evaluate the current evidence for the use of cannabis for reducing IOP in glaucoma. Methods: PubMed, Embase, and the Cochrane Database were searched along with references drawn from full text articles published before January 2018 for the best available evidence that met the inclusion criteria.Three authors independently evaluated and selected the articles that represented the best available evidence.The selected articles were chosen based on study methodology and the type of cannabis used for the treatment of glaucoma. Randomized Control Trials were preferred, although lacking. No studies directly compared cannabis to the current standard of care medications for lowering IOP. Results: Five randomized controlled trials were included as best available evidence although they used different routes of administration. All studies included compared cannabis to placebo. The studies evaluated showed a range of IOP lowering effects and side effects.Topical administration has shown conflicting results for the treatment of glaucoma.Conclusion:The many forms of cannabinoid administration have demonstrated variable levels of effectiveness. The variability of the studies indicates the need for more research. Specifically, larger sample sizes, and comparison of standardized cannabis to current standards of care instead of placebo are strongly encouraged.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".