Feasibility of a Randomized, Interventional Pilot Clinical Study of Oral Cannabinoids in People with HIV on Antiretroviral Therapy: CTNPT 028
Bibliographic record
Abstract
Cannabis-based medicines (CBMs) could help reduce systemic inflammation in people with HIV (PWH). In a prospective, randomized pilot study we enrolled participants from August 2021-April 2022 with HIV, aged ≥18 and on antiretroviral therapy and randomly assigned them to cannabidiol (CBD) ± Δ9-tetrahydrocannabinol (THC) capsules for 12 weeks with the primary objective being to assess safety and tolerability. Here we report on timeliness to study initiation, enrolment, compliance and retention rates. The target sample size was not reached. Two hundred and five individuals were approached, and 10 consented and were randomized; the rest refused (reasons: cannabis-related stigma/discomfort; too many study visits/insufficient time; unwillingness to undergo a "washout period" for three weeks) or were not eligible. The age of those randomized was 58 years (IQR 55-62); 80% were male. Only three met all criteria (30% enrolment compliance); seven were enrolled with minor protocol deviations. Compliance was excellent (100%). Eight (80%) participants completed the study; two (20%) were withdrawn for safety reasons (transaminitis and aggravation of pre-existing anemia). Time to study initiation and recruitment were the most challenging aspects. Ongoing work is required to reduce stigma related to CBMs. Future studies should find a balance between the requirements for safety monitoring and frequency of study visits.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".