Posttraumatic stress symptoms moderate the relationship between chronic pain and adverse cannabis outcomes: A pilot study
Bibliographic record
Abstract
Objective: Increasingly, cannabis is being prescribed/used to help manage posttraumatic stress symptoms (PTSS) or chronic pain, as cannabis has been argued to be beneficial for both types of symptoms. However, the evidence on efficacy is conflicting with evidence of risks mounting, leading some to caution against the use of cannabis for the management of PTSS and/or chronic pain. We examined the main and interactive effects of PTSS and chronic pain interference on adverse cannabis outcomes (a composite of cannabis use levels and cannabis use disorder, CUD, symptoms). We hypothesized that chronic pain interference and PTSS would each significantly predict adverse cannabis outcomes, and that chronic pain interference effects on adverse cannabis outcomes would be strongest among those with greater PTSS. Method: Forty-seven current cannabis users with trauma histories and chronic pain (34% male; mean age = 32.45 years) were assessed for current PTSS, daily chronic pain interference, past month cannabis use levels (grams), and CUD symptom count. Results: Moderator regression analyses demonstrated chronic pain interference significantly predicted the adverse cannabis outcomes composite, but only at high levels of PTSS. Conclusions: Cannabis users with trauma histories may be at greatest risk for heavier/more problematic cannabis use if they are experiencing both chronic pain interference and PTSS.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".