The impact of rheumatoid arthritis on implant-related complications and reoperation rates in spine surgery: A systematic review
Bibliographic record
Abstract
Objective: To systematically evaluate the impact of rheumatoid arthritis (RA) on implant-related complications and reoperation rates in patients undergoing spinal surgery. Methods: This systematic review followed the PRISMA guidelines and was registered on PROSPERO. A comprehensive search of PubMed, Embase, Scopus, and the Cochrane Library was conducted. Inclusion criteria encompassed observational studies comparing outcomes in adult RA patients and non-RA controls undergoing cervical, thoracic, or lumbar spine surgery. Data were extracted on surgical region, technique, complications, infections, and reoperation rates. Risk of bias was assessed using the Newcastle-Ottawa Scale. Results: Twelve comparative studies comprising over 60,000 patients were included. RA patients consistently exhibited higher rates of implant-related complications such as screw loosening, pseudarthrosis, and cage migration. They also had significantly increased rates of adjacent segment disease (ASD), surgical site infections, and reoperations. Factors contributing to these outcomes included poor bone quality, immunosuppression, and anatomical challenges. Cervical procedures were particularly impacted by vertebral artery anomalies and narrow pedicles, while lumbar fusions were associated with greater hidden blood loss and higher revision rates. RA patients also demonstrated more comorbidities, including osteoporosis, anemia, and cardiovascular disease, which may further increase perioperative risk. Conclusion: Rheumatoid arthritis is an independent risk factor for complications and surgical failure in spine surgery. Recognition of these risks should guide preoperative optimization, surgical planning, and postoperative care. A multidisciplinary approach is essential to improving outcomes in this high-risk population.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".