Recreational Cannabis Use Before and After Legalization in Women With Pelvic Pain
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prevalence and characteristics of recreational cannabis use in women with pelvic pain, and to examine the influence of cannabis legalization on these parameters. METHODS: We conducted a retrospective analysis of a prospective registry of women with self-reported moderate-to-severe pelvic pain referred to a tertiary care clinic in Vancouver, Canada, 2013-2019. We excluded patients aged 18 years or younger and those with unknown data on cannabis use. Demographic, clinical, and validated questionnaire data were extracted for two main analyses: 1) comparison of current cannabis users with current nonusers, and 2) comparison of current cannabis users who entered the registry before cannabis legalization (October 17, 2018) with those who entered the registry on or after legalization. RESULTS: Overall, 14.9% (509/3,426) of patients were classified as current cannabis users. Compared with nonusers, cannabis users were younger (P<.001), had lower levels of education (P<.001) and lower household income (P<.001), were taking opioids (P<.001), antiinflammatories (P=.003), neuromodulators (P=.020), and herbal medications (P<.001) more frequently. They had worse questionnaire scores for depression, anxiety, pain catastrophizing, quality of life, and pelvic pain severity (P<.001 for all). After cannabis legalization, prevalence of current cannabis use increased from 13.3% (366/2,760) to 21.5% (143/666) (P<.001). Compared with prelegalization, postlegalization users were associated with higher levels of education (P<.001), worse anxiety (P=.036), and worse pain catastrophizing (P<.001) scores. They were taking fewer antiinflammatories (P<.001), neuroleptics (P=.027) and daily opioids or narcotics (P=.026), but more herbal medications (P=.010). CONCLUSION: Recreational cannabis use increased among patients with pelvic pain after legalization in Canada. Cannabis users had worse pain-related morbidities. Postlegalization, cannabis users were less likely to require daily opioids compared with cannabis users before legalization. The role, perceived benefits, and possible risks of cannabis for pelvic pain require further investigation. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT02911090.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".