Association between history of cannabis use and outcomes after total hip or knee arthroplasty: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Cannabis use may be increasing as countries legalize it and it becomes socially acceptable. A history of cannabis use may increase risk of complications after various kinds of surgery and compromise functional recovery. Here we systematically reviewed and meta-analyzed available evidence on how history of cannabis use affects recovery after hip or knee arthroplasty (THA/TKA). Methods: The PubMed, EMBASE, and Web of Science databases were comprehensively searched and studies were selected and analyzed in accordance with the PRISMA guidelines. The methodological quality of included studies was assessed based on the Newcastle-Ottawa Scale, while quality of evidence was evaluated according to the "Grading of recommendations assessment, development, and evaluation" system. Data on various outcomes were pooled when appropriate and meta-analyzed. Results: The systematic review included 16 cohort studies involving 5.91 million patients. Meta-analysis linked history of cannabis use to higher risk of the following outcomes: revision (RR 1.68, 95% CI 1.31-2.16), mechanical loosening (RR 1.77, 95% CI 1.52-2.07), periprosthetic fracture (RR 1.85, 95% CI 1.38-2.48), dislocation (RR 2.10, 95% CI 1.18-3.73), cardiovascular events (RR 2.49, 95% CI 1.22-5.08), cerebrovascular events (RR 3.15, 95% CI 2.54-3.91), pneumonia (RR 3.97, 95% CI 3.49-4.51), respiratory failure (RR 4.10, 95% CI 3.38-4.97), urinary tract infection (RR 2.46, 95% CI 1.84-3.28), acute kidney injury (RR 3.25, 95% CI 2.94-3.60), venous thromboembolism (RR 1.48, 95% CI 1.34-1.63), and deep vein thrombosis (RR 1.42, 95% CI 1.19-1.70). In addition, cannabis use was associated with significantly greater risk of postoperative transfusion (RR 2.23, 95% CI 1.83-2.71) as well as higher hospitalization costs. Conclusion: History of cannabis use significantly increases the risk of numerous complications and transfusion after THA or TKA, leading to greater healthcare costs. Clinicians should consider these factors when treating cannabis users, and pre-surgical protocols should give special consideration to patients with history of cannbis 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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".