Knowledge of Cannabinoids among Patients, Physicians, and Pharmacists
Why this work is in the frame
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Bibliographic record
Abstract
Objective: Many patients hold false beliefs about cannabinoids.We evaluated their related beliefs and we also surveyed physicians and pharmacists about their opinions regarding cannabinoids. Materials and MethodStudy 1: 42 patients (mean age 39.1 years, SD=12.6,range 18 to 67) in urban methadone/suboxone clinics were surveyed via questionnaire about their use of cannabis and their knowledge of its potential medical applications and of its positive and negative properties.Study 2: We recruited 53 professionals (37 physicians and 16 pharmacists) to compare the utility and adverse side-effects of cannabinoids to those of other frequent non-opioid medications for pain, epilepsy, insomnia, and for loss of appetite in HIV positive patients. Results (both studies):Two-thirds (66.7%) of our patients reported using cannabis (71.4% of users via smoking, 46.4% in food, 28.6% as drops).The users knew significantly more (t=2.1,df=39, p=.043) legitimate medical applications of cannabis (mean=4.7,SD=2.9) than non-users (mean=2.1,SD=1.7).Most frequently listed medical applications were epilepsy (73.2%), cancer (70.7%), pain (65.9%), and arthritis (53.7%).However, only 52.4% of patients correctly attributed "drug induced psychosis" to tetrahydrocannabinol rather than to other cannabis constituents.Some erroneously attributed their "high" to cannabidiol (14.3%).The MDs and pharmacists who volunteered for our survey rated cannabinoids as being more free of adverse side-effects than some other commonly prescribed non-opioid medications for pain, insomnia, and for loss of appetite in HIV patients.Their ratings of cannabinoids for epilepsy were also relatively favourable.Conclusions: Patients need expert therapeutic guidance from their physicians and pharmacists to properly benefit from cannabinoids.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it