Challenges and considerations for treating PTSD with medicinal cannabis: the Australian clinician’s perspective
Bibliographic record
Abstract
INTRODUCTION: Preclinical and experimental research have provided promising evidence that medicinal cannabis may be efficacious in the treatment of posttraumatic stress disorder (PTSD). However, implementation of medicinal cannabis into routine clinical therapies may not be straightforward. AREAS COVERED: In this review, we describe some of the clinical, practical, and safety challenges that must be addressed for cannabis-based treatment of PTSD to be feasible in a real-world setting. These issues are especially prevalent if medicinal cannabis is to be combined with trauma-focused psychotherapy. EXPERT OPINION: Future consideration of the clinical and practical considerations of cannabis use in PTSD therapy will be essential to both the efficacy and safety of the treatment protocols that are being developed. These issues include dose timing and titration, potential for addiction, product formulation, windows of intervention, and route of administration. In particular, exposure therapy for PTSD involves recall of intense emotions, and the interaction between cannabis use and reliving of trauma memories must be explored in terms of patient safety and impact on therapeutic outcomes.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".