The Effect of Spinopelvic Parameters on the Development of Proximal Junctional Kyphosis in Early Onset: Mean 4.5-Year Follow-up
Bibliographic record
Abstract
BACKGROUND: Proximal junctional kyphosis (PJK) is a major complication after posterior spinal surgery. It is diagnosed radiographically based on a proximal junctional angle (PJA) and clinically when proximal extension is required. We hypothesized that abnormal spinopelvic alignment will increase the risk of PJK in children with early-onset scoliosis (EOS). METHODS: A retrospective study of 135 children with EOS from 2 registries, who were treated with distraction-based implants. Etiologies included 54 congenital, 10 neuromuscular, 37 syndromic, 32 idiopathic, and 2 unknown. A total of 89 rib-based and 46 spine-based surgeries were performed at a mean age of 5.3±2.83 years. On sagittal radiographs, spinopelvic parameters were measured preoperatively and at last follow-up: scoliosis angle (Cobb method, CA), thoracic kyphosis (TK), lumbar lordosis (LL), pelvic incidence (PI), pelvic tilt (PT), sacral slope and PJA. Radiographic PJK was defined as PJA≥10 degrees and PJA≥10 degrees greater than preoperative measurement. The requirement for the proximal extension of the upper instrumented vertebrae was considered a proximal junctional failure (PJF). Analysis of risk factors for the development of PJK and PJF was performed. RESULTS: At final follow-up (mean: 4.5±2.6 y), CA decreased (P<0.005), LL (P=0.029), and PI (P<0.005) increased, whereas PI-LL (pelvic incidence minus lumbar lordosis) did not change (P=0.706). Overall, 38% of children developed radiographic PJK and 18% developed PJF. Preoperative TK>50 degrees was a risk factor for the development of radiographic PJK (relative risk: 1.67, P=0.04). Children with high postoperative CA [hazard ratio (HR): 1.03, P=0.015], postoperative PT≥30 degrees (HR: 2.77, P=0.043), PI-LL>20 degrees (HR: 2.92, P=0.034), as well as greater preoperative to postoperative changes in PT (HR: 1.05, P=0.004), PI (HR: 1.06, P=0.0004) and PI-LL (HR: 1.03, P=0.013) were more likely to develop PJF. Children with rib-based constructs were less likely to develop radiographic PJK compared with children with spine-based distraction constructs (31% vs. 54%, respectively, P=0.038). CONCLUSIONS: In EOS patients undergoing growth-friendly surgery for EOS, preoperative TK>50 degrees was associated with increased risk for radiographic PJK. Postoperative PI-LL>20 degrees, PT≥30 degrees, and overcorrection of PT and PI-LL increased risk for PJF. Rib-based distraction construct decreased the risk for radiographic PJK in contrast with the spine-based constructs. LEVEL OF EVIDENCE: Level III.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".