Preoperative Sacroiliac Joint Pain in Adult Spinal Deformity Patients
Bibliographic record
Abstract
INTRODUCTION: The sacroiliac joint (SIJ) is a potential source of pain in the ASD population. Incidence and predictors of preoperative SIJ pain and rates of resolution with surgery in the ASD population are not well understood. METHODS: A prospective, multicenter database of surgically treated ASD patients was queried for baseline SIJ pain at the preoperative assessment. SIJ pain was defined as self-reported back pain in the posterior superior iliac spine region scored ≥4 out of 10 and ≥3 of 5 positive provocative SIJ maneuvers. Demographic data, spinal alignment parameters, and health assessments were assessed using Wilcoxon and χ 2 analysis. Predictors of preoperative SIJ pain were assessed with univariate and multivariate logistic regression. RESULTS: A total of 735 patients were included with a mean (SD) age of 61.3 (15.3) years, BMI of 27.6 (5.4), Edmonton Frailty Score (EFS) of 3.4 (2.5), and Charlson Comorbidity Index (CCI) of 1.1 (1.8). A total of 65% were female and 6% were tobacco users. A total of 411 patients had self-reported PSIS pain and 53 patients (7.2%) had preoperative SIJ pain as assessed by SIJ maneuver testing. SIJ pain was not associated with history of prior lumbosacral fusion ( P =0.23). Patients with SIJ pain had higher BMI (30.0 vs . 27.4, P =0.004), preoperative pain medication usage (92.5% vs . 77.7%, P =0.02), EFS (4.6 vs . 3.3, P <0.001), and CCI (1.6 vs . 1.0, P =0.006) as well as lower L4-S1 lordosis (28.7 vs . 34.5, P =0.02) and greater L1 pelvic angle (14.5 vs . 10.8, P =0.03). After variable selection with univariate regression, multivariate logistic regression identified higher BMI (OR 1.06, P =0.033) as a significant predictor of SIJ pain at preoperative. In the patient cohort with SIJ pain at preoperative, 91.7% reported no SIJ pain at 1-year follow-up. 11/53 (20.8%) patients with baseline pain and SIJ fusion performed concurrently with ASD surgery had 100% resolution of SIJ pain in this cohort; however, there was no significant difference in pain resolution between patients with SIJ fusion and those without ( P =1). CONCLUSION: We found a lower prevalence of preoperative SIJ pain in ASD patients than what has been historically reported, present in 7.2% of patients. Higher BMI was a predictor of preoperative SIJ pain in this population. ASD surgery led to resolution of SIJ pain in >90% of patients at 1-year follow-up.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".