Cannabis in Hematology Survey Study (CHESS): A Longitudinal Investigation on Uses, Attitudes, and Outcomes of Cannabis Among Hematology Patients Undergoing Hematopoietic Stem Cell Transplant
Bibliographic record
Abstract
Cancer patients use cannabis for medicinal purposes; however, few studies have examined hematology patients’ use of cannabis and no research to our knowledge has investigated the use of cannabis amongst hematology patients before and after hematopoietic stem cell transplant (HCT). The purpose of this longitudinal survey study was to assess aspects of cannabis use in patients who underwent HCT. Eligible patients (N = 30) completed two surveys before and 90 days following their HCT. The surveys inquired about several aspects of cannabis (e.g., rate of use, beliefs and attitudes, access to information) and physical and psychological outcomes (e.g., anxiety, comorbidities, graft-versus-host-disease). Rates of cannabis use decreased following HCT (n = 14, 46% to n = 11, 40%). Conversations on cannabis that were initiated by an oncology health care provider increased post-transplant (n = 3, 10% to n = 11, 37%). This coincided with fewer who were smoking cannabis as a primary consumption method (n = 5, 38 to n = 2, 18) and an increase in the use of pharmaceutical cannabinoid products (n = 4, 13% to n = 6, 21%) as well as oils and topicals. Of the total sample, 63% (n = 17) experienced post-treatment complications and 33% (n = 10) developed GVHD, six of whom where recent cannabis users. This study provided insight into cannabis use amongst HCT patients and warrants further research with this population, including more exploration of the relationship between GVHD and cannabis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".