Incidence and risk factors of new-onset sacroiliac joint pain after spinal surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose: A systematic review and meta-analysis for incidence and risk factors of new-onset sacroiliac joint pain (SIJP) after spinal surgery aimed to provide evidence-based medical references for its early prevention, timely intervention, and appropriate treatment. Methodology: The protocol of the systematic review and meta-analysis was registered in the International Prospective Register of Systematic Review (PROSPERO) with the PROSPERO ID (CRD42023463177). Relevant studies were searched to January 2024 from the databases of PubMed, Embase, Cochrane Library, and Web of Science, and the types of studies were cohort studies, case-control studies, and cross-sectional studies. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) and the Cross-Sectional/Prevalence Study Quality recommended by the Agency for Healthcare Research and Quality (AHRQ). Two authors conducted studies search, data extraction, and quality assessment independently. Meta-analyses were done using Stata 14.0 software. Results: Twelve observational studies with 3,570 spinal surgery patients were included. Ten were case-control studies, one was a cross-sectional study, and another was a cohort study, all of which were of moderate quality and above. The results of the meta-analysis showed that the incidence of new-onset SIJP after spinal surgery was 9.40%; females, no. of surgical segments, fusion to the sacrum, and postoperative pelvic tilt (PT) were significantly associated with the new-onset SIJP after spinal surgery. Meta-analyses for preoperative and postoperative controls of spondylopelvic parameters showed that postoperative lumbar lordosis (LL) in the SIJP group and postoperative LL and sacral slope (SS) of patients in the NoSIJP group had significant differences from preoperative. Conclusion: Available evidence suggests that an increased risk of new-onset SIJP after spinal surgery is associated with sex, multi-segmental surgery, fusion to the sacrum, and greater postoperative PT.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.052 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".