Controlled administration of cannabis to mitigate cannabis-attributable harm among recreational users: a quasi-experimental study in Germany
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> New approaches are required to slow down or reverse increasing trends of levels of delta-9-tetrahydrocannabinol (THC) and cannabis-attributable hospitalizations in Germany. Legal access to cannabis may constitute one viable effective policy response; however, available evidence does not suffice to inform a regulation model for Germany. The proposed study aims to reduce harm for cannabis users through legal access to herbal cannabis through pharmacies. </ns4:p> <ns4:p> <ns4:bold>Protocol:</ns4:bold> A quasi-experimental study comparing cannabis users with legal access to herbal cannabis (Berlin, intervention group) to those without legal access (Hamburg, control group) (total N=698). As the primary outcome, we hypothesize that: 1) illegal THC consumption will reduce by at least 50% in the intervention group and 2) total THC exposure in the intervention group will be reduced by at least 10% lower than that of the control group, taking into account baseline values. Secondary outcomes comprise measures of frequency of use, THC-impaired driving, and mode of administration. Paired t-tests and multilevel regression models will be performed for statistical analyses. </ns4:p> <ns4:p> <ns4:bold>Discussion:</ns4:bold> This study proposal is currently being reviewed by the ‘Federal Institute for Drugs and Medical Devices’ – the body responsible for approving research studies on classified substances, including cannabis. Upon approval and prior to the start of the study, a full ethical review will be undertaken. Results may inform a regulation model for Germany and other jurisdictions and are expected to deepen the understanding of the effects of legal access to cannabis. </ns4:p> <ns4:p> <ns4:bold>Pre-registration:</ns4:bold> German Clinical Trials Register (DRKS), DRKS00020829 </ns4:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".