Prevalence and Characteristics of Medicinal Cannabis Use among Chronic Pain Patients; A Post- Legalization Study in a Tertiary Care Setting in Thailand
Bibliographic record
Abstract
Objective: Cannabinoid products have been applied for numerous medical conditions, including chronic pain. Thailand was the first country in South East Asia to legalize medical cannabinoids. This study aims to explore prevalence, characters, attitude, side effects of medical cannabinoid use, and pain-related outcome among the chronic cancer and non-cancer pain population at Siriraj Hospital. Materials and Methods: 200 chronic cancer pain and 670 chronic noncancer pain patients were collected by questionnaires and interviews. Data included demographic data, clinical diagnosis, pain treatment, knowledge, attitude, pattern of use, side effects and quality of life of cannabinoid extracts. Results: Prevalence of active cannabis user was 15% in chronic cancer pain and 3.1% in noncancer pain. Oil extract sublingual was the most common form. Pain control was the most common initial reason for usage. No serious side effects were reported. Common side effects were dry oral mucosa, drowsiness, and headache. The most common source was obtained from friends. 36% of the patients believed they had enough understanding of medical cannabis, while 68.5% agreed that it is appropriate to use in Thailand. In cancer patients, the Edmonton Symptom Assessment System (ESAS) subscale for lack of appetite, anxiety, and subscale for a brief pain inventory (BPI) for enjoyment of life were higher among active users. In patients with noncancer pain, only the mood subscale BPI was lower among active users. Conclusion: Medical cannabis usage is common compared with general population in Thai patients with chronic pain and may be associated with increased pain interference and cancer-related symptoms. Nonmedical license prescription and nonmedical license cannabis products were common in Thailand.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".