Cannabis use in gynecologic cancer patients in a Canadian cancer center
Bibliographic record
Abstract
Objective: The primary objective of this study was to estimate the prevalence of cannabis use in patients with gynecologic malignancies and to describe patterns of cannabis use. Secondary objectives included identifying sources of cannabis information used by patients. Methods: This is a single institution cross sectional survey conducted in Calgary, Alberta. Patients with a current or prior gynecologic cancer diagnosis were considered for inclusion. Planned analysis included descriptive statistics of patient demographics, and the patterns of cannabis use were described using frequencies and proportions. Results: Forty-six patients participated in the survey. The most common disease sites were ovarian cancer and uterine cancer, with the majority of patients receiving chemotherapy as part of their treatment (n = 35). Seventeen participants were current cannabis users (37%). The most common symptoms participants used cannabis for were pain (9/17), anxiety (9/17), and insomnia (9/17).Most patients using cannabis did not have a prescription and obtained their cannabis from a recreational dispensary (11/17). Many participants using cannabis had not talked to their doctor about cannabis (9/17). Instead, the most common sources of information about cannabis were cannabis retailers (20/46), and friends/family (20/46). Over 50% of patients would be interested in discussing cannabis if their physician broached the subject (26/46). Conclusions: The results from this survey indicate that patients would like to talk to their oncologist about cannabis. Further research is needed to inform physician training and direct patient education to ensure that patients have access to unbiased, evidence-based information to make decisions about cannabis use.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".