Preoperative Cannabis Use Is Associated With Elevated Risk for Complications and Revision Surgery After Anterior Cervical Diskectomy and Fusion
Bibliographic record
Abstract
BACKGROUND: Cannabis use has increased markedly in recent decades, with US adult usage doubling from 4.1% to 9.5% between 2001 and 2013, and 43% of young adults reporting use by 2021. As legalization expands and cannabis becomes more mainstream, its perioperative implications have become clinically relevant. Although the orthopaedic literature has begun exploring the effect of cannabis use, its effect on anterior cervical diskectomy and fusion (ACDF) remains underinvestigated. Given the widespread adoption of ACDF and the projected rise in spinal procedures, understanding cannabis-related risks is essential to optimizing surgical outcomes. METHODS: This retrospective cohort study used the TriNetX Research Network to identify patients undergoing primary ACDF from 2003 to 2023. Patients with cannabis use within 3 months before surgery were compared with matched nonusers using 1:1 propensity-score matching across key demographics and comorbidities. Outcomes included 90-day medical complications and healthcare utilization, as well as 2-year surgical outcomes including revision surgery, pseudarthrosis, and implant failure. RESULTS: After matching (n = 2,169 per group), cannabis users demonstrated significantly higher 90-day rates of dysphagia (7.10% vs. 5.40%, P = 0.03), infection (1.60% vs. 0.90%, P = 0.04), postprocedural pain (7.20% vs. 4.10%, P < 0.001), opioid use (48.0% vs. 25.0%, P = 0.03), and readmission (3.90% vs. 2.70%, P = 0.04). At 2 years, cannabis users had higher rates of revision surgery (7.82% vs. 5.15%, P = 0.001), pseudarthrosis (10.10% vs. 8.25%, P = 0.04), and implant failure (4.31% vs. 2.78%, P = 0.01). CONCLUSION: Preoperative cannabis use is associated with higher rates of medical and surgical complications, as well as an increased risk of revision surgery after ACDF. With cannabis use projected to rise markedly among surgical patients, these findings highlight its potential role as a modifiable risk factor, similar to tobacco use. Recognizing this association, incorporating evidence-based risk stratification, cannabis cessation counseling, and optimized perioperative care pathways may help reduce complication rates and improve fusion outcomes in patients undergoing ACDF.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".