The Efficacy of Marijuana Use for Pain Relief in Adults With Sickle Cell Disease: A Systematic Review
Bibliographic record
Abstract
Sickle Cell Disease (SCD) is a disease that affects many around the world and often accounts for frequent hospital admissions every year, secondary to uncontrolled pain. Marijuana is increasingly being used for its medicinal ability to treat pain in chronic medical conditions. Therefore, it is imperative to determine how effective it would be in providing pain relief to patients with SCD. We systematically screened five databases for relevant data: PubMed, Medline, PubMed Central (PMC), Cochrane Library, and Google Scholar. The inclusion and exclusion criteria were implemented. A quality appraisal was then done using the Cochrane Bias assessment for randomized controlled trials (RCTs), Newcastle-Ottawa tool for observational studies, and Scale for the Assessment of Narrative Review Articles (SANRA) checklist for traditional review articles. From seven articles, information was gathered; one systematic review, one RCT, two surveys, one cross-sectional study, one retrospective study, and one questionnaire-based study. Our review concluded that based on the literature assessed, marijuana use in SCD patients either worsened their painful crises or offered little to no help compared to opioids or hydroxyurea usage. There were limited RCTs published in addition to papers investigating the long-term effects of marijuana use in SCD. We hope that further data is gathered in these areas to sufficiently address whether cannabis use is efficacious for pain relief in patients with SCD.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".