Adverse Impact of Smoking on Spine Fusion and Patient-Reported Outcomes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Study design Systematic review and meta-analysis. Objective This systematic review with meta-analysis is aimed at evaluating the impact of smoking (tobacco) on spinal fusion rates and the resulting PROMs. Methods Following the PRISMA guidelines, a systematic literature search was conducted in 4 databases. Studies focused on adult smokers vs non-smokers undergoing spinal fusion. Odds ratios (ORs) were calculated for dichotomous variables and mean differences or standardized mean differences for continuous variables. The primary outcomes assessed were non-union/pseudoarthrosis incidence and PROMs. Results A total of 29 studies were included in this analysis. The unadjusted incidence of pseudoarthrosis was significantly higher in smokers than in non-smokers (OR 1.97, 95% CI 1.55-2.52, P < 0.001). Subgroup analysis revealed significant differences in the cervical (OR 2.09, 95% CI 1.27-3.44, P < 0.05) and lumbar (OR 1.97, 95% CI 1.45-2.68, P < 0.001) regions. Adjusted analysis also showed a significantly higher incidence of pseudoarthrosis in smokers (OR 1.38, 95% CI 1.12-1.72, P < 0.05). Changes in ODI, VAS, EQ-5D, and SF-12 and SF-36, consistently favored nonsmoking patients. Smoking was associated with a lower rate of returning to work (OR 0.70, 95% CI 0.54-0.90, P < 0.05), and in the lumbar subgroup, reduced satisfaction (OR 0.24, 95% CI 0.12-0.49, P < 0.001). Former smokers (smoking cessation for at least 1 year prior to surgery) did not show significant differences compared to nonsmokers in terms of pseudarthrosis rate or pain scores. Conclusion Smoking is associated with an increased risk of pseudarthrosis and poorer PROMs after spinal fusion surgery. Healthcare providers should emphasize smoking cessation interventions to improve surgical outcomes and patient satisfaction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".