Role of the endocannabinoid system in the pathophysiology of endometriosis and therapeutic implications
Bibliographic record
Abstract
Endometriosis patients experience debilitating chronic pain, and the first-line treatment is ineffective at managing symptoms. Although surgical removal of the lesions provides temporary relief, more than 50% of the patients experience disease recurrence. Despite being a leading cause of hysterectomy, endometriosis lacks satisfactory treatments and a cure. Another challenge is the poor understanding of disease pathophysiology which adds to the delays in diagnosis and overall compromised quality of life. Endometriosis patients are in dire need of an effective therapeutic strategy that is both economical and effective in managing symptoms, while fertility is unaffected. Endocannabinoids and phytocannabinoids possess anti-inflammatory, anti-nociceptive, and anti-proliferative properties that may prove beneficial for endometriosis management, given that inflammation, vascularization, and pain are hallmark features of endometriosis. Endocannabinoids are a complex network of molecules that play a central role in physiological processes including homeostasis and tissue repair, but endocannabinoids have also been associated in the pathophysiology of several chronic inflammatory diseases including endometriosis and cancers. The lack of satisfactory treatment options combined with the recent legalization of recreational cannabinoids in some parts of the world has led to a rise in self-management strategies including the use of cannabinoids for endometriosis-related pain and other symptoms. In this review, we provide a comprehensive overview of endocannabinoids with a focus on their potential roles in the pathophysiology of endometriosis. We further provide evidence-driven perspectives on the current state of knowledge on endometriosis-associated pain, inflammation, and therapeutic avenues exploiting the endocannabinoid system for its management.
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How this classification was reachedexpand
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".