Medical Assessment of Cannabis Efficacy and Side-effects Scale (MACESS©): a simple evidence-based scale to determine clinical benefits and adverse events following medical cannabis use
Bibliographic record
Abstract
Medical cannabis has been proposed to benefit patients in a wide range of conditions, including chronic pain, psychological conditions including anxiety, and sleep issues. It is available via prescription in multiple countries, and in Canada it is available legally in both stores and on-line. The range of methods by which it can be consumed are large including (1) inhalation via smoking or using vapourizers, (2) swallowed as an oil or within a food or liquid, or (3) absorbed through the skin including as a patch. As the range of products increases, a major problem is that there is no standardized scale to determine changes, both positive and negative, induced by medical cannabis products. This also means that there is no method to compare products, and the Medical Assessment of Cannabis Efficacy and Side-effects Scale (MACESS©) is specifically designed to address this issue. Following a comprehensive medical literature search and review, the most relevant clinical benefits and adverse events following the use of cannabis for medical purposes were determined. Following this key items were identified, with the scale being designed to measure these. The scale consists of 25 questions with each question being scored from 0 – 4, giving a range of total potential scores between 0 – 100. With the MACESS©a high score indicate a well tolerated and effective product, while a low scores indicates significant side-effects or adverse events and/or lack of positive clinical changes. It is available online and can be used for research, to measure change following prospective use in individuals or groups, and for cross-sectional information. It is intended to support both individual users and researchers examining benefits and problems with specific medical products, and can provide an easily understood single number for overall product comparisons.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".