Topical cannabis‐based medicines – A novel paradigm and treatment for non‐uremic calciphylaxis leg ulcers: An open label trial
Bibliographic record
Abstract
Non-Uremic Calciphylaxis (NUC) is a rare condition that often manifests as intractable and painful integumentary wounds, afflicting patients with a high burden of co-morbidity. The Endocannabinoid System (ECS) is a ubiquitous signalling system that is theorised to be dysregulated within wound beds and associated peri-wound tissues. Preclinical research has shown that the dominant chemical classes derived from the cannabis plant, cannabinoids, terpenes, and flavonoids, interact with the integumentary ECS to promote wound closure and analgesia. This is a prospective open label cohort study involving two elderly Caucasian females with recalcitrant NUC leg ulcers of greater than 6 months duration. Topical Cannabis-Based Medicines (TCBM) composed of cannabinoids, terpenes, and flavonoids were applied daily to both the wound bed and peri-wound tissues until complete wound closure was achieved. Wounds were photographed regularly, and the digital images were subjected to planimetric analysis to objectively quantify the degree of granulation and epithelization. Analgesic utilisation, as a surrogate/proxy for pain scores, was also tracked. The cohort had a mean M3 multimorbidity index score of 3.31. Complete wound closure was achieved in a mean of 76.3 days. Additionally, no analgesics were required after a mean of 63 days. The treatments were well tolerated with no adverse reactions. The positive results demonstrated in very challenging wounds such as NUC, among highly complex patients, suggest that TCBM may have an even broader role within integumentary and wound management. This treatment paradigm warrants being trialled in other wound types and classes, and ultimately should be subjected to randomised controlled trials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".