No Evidence of Altered Reactivity to Experimentally Induced Pain Among Regular Cannabis Users
Bibliographic record
Abstract
OBJECTIVES: Recent years have seen an increase in the adoption of cannabinoid medicines, which have demonstrated effectiveness for the treatment of chronic pain. However, the extent to which frequent cannabis use (CU) influences sensitivity to acute pain has not been systematically examined. Such a determination is clinically relevant in light of hypersensitivity to pain associated with prolonged use of other analgesics such as opioids, and reports of increased pain sensitivity to experimentally induced pain during acute cannabis intoxication. This study explored differences in measures of pain intensity and tolerance. The authors hypothesized that individuals who report frequent CU would demonstrate greater experimental pain sensitivity. MATERIALS AND METHODS: Frequent cannabis users (≥3× per week; n=40) and nonusers (n=40) were compared on pain sensitivity, pain tolerance, and pain intensity in response to a cold-pressor task. Group differences were examined. RESULTS: Frequent CU was not associated with hyperalgesia as cannabis users and nonusers did not exhibit differences on measures of pain tolerance (t (78)=-0.05; P=0.96), sensitivity (t (78)=-0.83; P=0.41), or intensity (t (78)=0.36; P=0.72). DISCUSSION: Frequent cannabis users did not demonstrate hyperalgesia. This finding should help to inform evaluations of the relative harms and benefits of cannabis analgesic therapies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".