Topical cannabis‐based medicines – A novel adjuvant treatment for venous leg ulcers: An open‐label trial
Bibliographic record
Abstract
Venous leg ulcers are highly prevalent lower limb integumentary wounds that remain challenging to heal despite the use of evidence-based compression therapies. A multitude of adjuvant treatments has been studied but none have demonstrated enough efficacy to gain adoption into treatment guidelines. Global attention on Cannabis-Based Therapies is increasing and has been driven by quantum scientific advancements in the understanding of the endocannabinoid signalling system. Topical Cannabis-Based Medicines represent a novel treatment paradigm for venous leg ulcers in terms of promoting wound closure. Fourteen complex patients with sixteen recalcitrant leg ulcers were treated with Topical Cannabis-Based Medicines in conjunction with compression bandaging, every second day, to both wound bed and peri-wound tissues. The cohort had a mean age of 75.8 years and was medically complex as reflected by a mean M3 multimorbidity index score of 2.94 and a mean Palliative Performance Scale score of 67.1%. Complete wound closure, defined as being fully epithelialized, was achieved among 11 patients (79%) and 13 wounds (81%) within a median of 34 days. All three remaining patients demonstrated progressive healing trends but were lost to follow-up. The treatments were well tolerated, and no significant adverse reactions were experienced. The rapid wound closure of previously non-healing venous leg ulcers among elderly and highly complex patients suggests that Topical Cannabis-Based Medicines may become effective adjuvants in conjunction with compression therapy. This may also indicate that they may have an even broader role within integumentary and wound management. Therefore, this treatment paradigm warrants being subjected to 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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".