Potential for endocannabinoid system modulation in ocular pain and inflammation: filling the gaps in current pharmacological options
Bibliographic record
Abstract
Challenges in the management of ocular pain are an underappreciated topic. Currently available therapeutics lack both efficacy and clear guidelines for their use, with many also possessing unacceptable side effects. Promising novel agents would offer analgesic, anti-inflammatory, and possibly neuroprotective actions; have favorable ocular safety profiles; and show potential in managing neuropathic pain. Growing evidence supports a link between the endocannabinoid system (ECS) and a range of physiological and disease processes, notably those involving inflammation and pain. Both preclinical and clinical data suggest analgesic and anti-inflammatory actions of cannabinoids and ECS-modifying drugs in chronic pain conditions, including those of neuropathic origin. This review will examine existing evidence for the anatomical and physiological basis of ocular pain, specifically, ocular surface disease and the development of chronic ocular pain. The mechanism of action, efficacy, and limitations of currently available treatments will be discussed, and current knowledge related to ECS-modulation of ocular pain and inflammatory disease will be summarized. A perspective will be provided on the future directions of ECS research in terms of developing cannabinoid therapeutics for ocular pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".