Importance of UIV+1 Slope on Shear Force and Risk of PJK Development: Mathematical and Musculoskeletal Modeling With Clinical Confirmation
Bibliographic record
Abstract
Study designRetrospective cohort study.ObjectivesDespite several recognized risk factors, PJK remains a common postoperative occurrence in adult spinal deformity surgery. This study aims to identify a significant preoperative sagittal alignment parameter that is associated with vertebral loading at the proximal junction and can predict the likelihood of proximal junctional kyphosis (PJK).MethodsFifty-five consecutive patients who underwent fusion surgery from lower thoracic (T9-T11) to the pelvis were included in this retrospective study. Based on the preoperative slope of the level above the upper-instrumented vertebrae (UIV+1), patients were stratified into two groups: posterior and anterior UIV+1 slope. Patient-specific musculoskeletal (MSK) models were created from pre and immediate postoperative EOS images. Pre and postoperative radiographic parameters, and normalized vertebral loading at UIV and UIV+1 were compared.Results6/21 (28.6%) patients with posterior UIV+1 slope developed PJK, while the incidence of PJK for anterior UIV+1 slope was 24/34 (70.6%). Average normalized shear difference of UIV+1 in PJK and non-PJK patients was 0.19 and 0.1 for posterior slope patients, whereas for anterior slope patients, they were 0.3 and 0.18 respectively. Statistically significant associations were revealed between UIV+1 slope, shear force and risk of PJK development.ConclusionUtilizing mathematical and musculoskeletal analyses with clinical correlation, we were able to demonstrate UIV+1 slope as a local sagittal alignment risk factor that affects shear force at UIV/UIV+1, influencing PJK risk. Considering UIV+1 slope as a local alignment risk factor for PJK development may provide insight regarding UIV selection and alignment goals during preoperative planning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".