Medical Cannabis Prescription Practices and Quality of Life in Thai Patients: A Nationwide Prospective Observational Cohort Study
Bibliographic record
Abstract
Introduction: The legalization of cannabis in Thailand has renewed interest in its traditional medical use. This study aimed to explore the prescribing patterns of traditional practitioners and assess the impact of cannabis oil on patients’ quality of life, with a specific focus on comparing outcomes between cancer and non-cancer patients. Methods: We conducted a prospective observational cohort study across 30 sites in 21 Thai provinces to analyze the use of “Ganja Oil,” a cannabis extract in 10% coconut oil, prescribed for symptoms like pain, anorexia, and insomnia across a diverse patient group, including cancer and migraines. Quality of life was assessed using the Edmonton Symptom Assessment Scale (ESAS) and EQ-5D-5L at baseline, 1, 2, and 3 months. The study included a predefined subgroup analysis to compare the effects on cancer versus non-cancer patients. Data management was facilitated through Research Electronic Data Capture (REDCap), with statistical analysis performed using Stata/MP. Results: Among 21,284 participants, the mean age was 54.10 ± 15.32 years, with 52.49% being male. The baseline EQ-5D-5L index was 0.85 ± 0.24. Significant differences in EQ-5D-5L indices were seen between cancer patients (0.79 ± 0.32) and non-cancer patients (0.85 ± 0.23; p < 0.001). ESAS scores also differed significantly between these groups for all symptoms, except anxiety. The most frequent prescription of Ganja Oil was oral administration at bedtime (88.26%), with the predominant dosage being three drops daily, approximately 0.204 mg of tetrahydrocannabinol in total. Posttreatment, significant improvements were noted: the EQ-5D-5L index increased by 0.11 points (95% CI: 0.11, 0.11; p < 0.001) overall, 0.13 points (95% CI: 0.12, 0.14; p < 0.001) for cancer patients, and 0.11 points (95% CI: 0.10, 0.11; p < 0.001) for non-cancer patients. ESAS pain scores improved by −2.66 points (95% CI: −2.71, −2.61; p < 0.001) overall, −2.01 points (95% CI: −2.16, −1.87; p < 0.001) for cancer patients, and −2.75 points (95% CI: −2.80, −2.70; p < 0.001) for non-cancer patients, with similar significant improvements in other symptoms. Conclusion: Our study indicates potential benefits of Ganja Oil for improving quality of life among Thai patients, as a complementary treatment. These findings must be viewed in light of the study’s design limitations. Further controlled studies are essential to ascertain its efficacy and inform dosing guidelines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".