Medicinal Use of Different Cannabis Strains: Results from a Large Prospective Survey in Germany
Bibliographic record
Abstract
Abstract Background Up to now, it is unclear whether different medicinal cannabis (MC) strains are differently efficacious across different medical conditions. In this study, the effectiveness of different MC strains was compared depending on the disease to be treated. Methods This was an online survey conducted in Germany between June 2020 and August 2020. Patients were allowed to participate only if they received a cannabis-based treatment from pharmacies in the form of cannabis flowers prescribed by a physician. Results The survey was completed by n=1,028 participants. Most participants (58%) have used MC for more than 1 year, on average, 5.9 different strains. Bedrocan (pure tetrahydrocannabinol to pure cannabidiol [THC:CBD]=22:<1) was the most frequently prescribed strain, followed by Bakerstreet (THC:CBD=19:<1) and Pedanios 22/1 (THC:CBD=22:1). The most frequent conditions MC was prescribed for were different pain disorders, psychiatric and neurological diseases, and gastrointestinal symptoms. Overall, the mean patient-reported effectiveness was 80.1% (range, 0–100%). A regression model revealed no association between the patient-reported effectiveness and the variety. Furthermore, no influence of the disease on the choice of the MC strain was detected. On average, 2.1 side effects were reported (most commonly dry mouth (19.5%), increased appetite (17.1%), and tiredness (13.0%)). However, 29% of participants did not report any side effects. Only 398 participants (38.7%) indicated that costs for MC were covered by their health insurance. Conclusions Patients self-reported very good efficacy and tolerability of MC. There was no evidence suggesting that specific MC strains are superior depending on the disease to be treated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".