Cannabis and glaucoma: A literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it