The Potential Use of Cannabis in Tissue Fibrosis
Bibliographic record
Abstract
Fibrosis is a condition characterized by thickening or/and scarring of various tissues. Fibrosis may develop in almost all tissues and organs, and it may be one of the leading causes of morbidity and mortality. It provokes excessive scarring that excels the usual wound healing response to trauma in numerous organs. Currently, very little can be done to prevent tissue fibrosis, and it is almost impossible to reverse it. Anti-inflammatory and immunosuppressive drugs are among the few treatments that may be efficient in preventing fibrosis. Numerous publications suggest that cannabinoids and extracts of Cannabis sativa have potent anti-inflammatory and anti-fibrogenic properties. In this review, we describe the types and mechanisms of fibrosis in various tissues and discuss various strategies for prevention and dealing with tissue fibrosis. We further introduce cannabinoids and their potential for the prevention and treatment of fibrosis, and therefore for extending healthy lifespan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".