The impact of tetrahydrocannabinol on central pain modulation in chronic pain: a randomized clinical comparative study of offset analgesia and conditioned pain modulation in fibromyalgia
Bibliographic record
Abstract
BACKGROUND: Tetrahydrocannabinol (THC) has shown efficacy in alleviating chronic pain, particularly in disorders characterized by central sensitization. Offset analgesia (OA) and conditioned pain modulation (CPM) are key biomarkers used to evaluate central pain modulation. This study aimed to compare the effects of THC on OA and CPM in fibromyalgia syndrome (FMS), a prototypical condition of central sensitization. METHODS: In a randomized, double-blind, placebo-controlled crossover design, 23 FMS patients participated in two experimental sessions. Each session included the McGill Pain Questionnaire, visual analogue scale (VAS) assessments, and evaluations of OA and CPM, conducted both before and after sublingual administration of either THC (0.2 mg/kg) or placebo. RESULTS: THC significantly reduced spontaneous pain ratings on the McGill scale compared to both baseline and placebo (P = 0.01 and P = 0.02, respectively). THC also significantly enhanced OA relative to baseline and placebo (P = 0.04 and P = 0.008), while no effect was observed on CPM (P = 0.27). Notably, baseline OA magnitude significantly predicted THC-induced pain relief (R² = 0.404, P = 0.003), whereas CPM did not show a significant association (P = 0.121). CONCLUSIONS: This is the first study to evaluate THC’s distinct effects on central pain modulation using both OA and CPM. THC selectively enhanced OA without influencing CPM, highlighting differential neural mechanisms underlying these paradigms. Furthermore, OA predicted treatment response, suggesting its potential as a biomarker for personalized cannabinoid-based therapies in FMS and other central sensitization disorders. TRIAL REGISTRATION: The study was prospectively registered on ClinicalTrials.gov (ID: NCT05644054) at 1.1.2023. Further details can be found at: https://clinicaltrials.gov/study/NCT05644054?locStr=Israel&country=Israel&cond=fibromyalgi215a%20&intr=THC&aggFilters=status:not%20rec&rank=1 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".