The Association Between Adjacent Forces and the Development of Proximal Junctional Kyphosis: A Musculoskeletal Analysis
Bibliographic record
Abstract
Study DesignBiomechanical modeling study.ObjectivesDespite several recognized risk factors for proximal junctional kyphosis (PJK), its causative mechanism remains unknown. There are limited biomechanical studies to elucidate the association of sagittal alignment with PJK. Our aim is to determine the association of adjacent forces with PJK development.MethodsASD patients operated on with posterior fusion from T9-T11 to the pelvis and had 2 year follow-up were included in this retrospective study. Patient-specific musculoskeletal models were created from EOS images. High vs low normalized vertebral forces were defined based on a threshold of 0.2 for shear and 0.5 for compression. The load ratio was defined as the ratio of immediate postoperative to preoperative vertebral forces.ResultsA total of 35 patients (19 PJK, 16 Non-PJK) were included. PJK patients had statistically significant global malalignment with respect to global tilt (GT), T1 pelvic angle (TPA) and C2-UIV+1PA. A high-risk zone for the development of PJK is defined as high normalized shear and/or compression with a high load ratio. While none of the non-PJK patients were in the high-risk zone, 5 PJK patients were not in that zone, suggesting factors other than alignment may have contributed to this adverse event. Despite low shear for 3 of these non-PJK patients, they experienced high shear ratio.ConclusionsThe proposed thresholds were accurate in 86% of patients who developed PJK. These findings suggest postoperative shear at UIV+1 as an important risk factor for PJK. Keeping shear force low in alignment will help surgeons reduce PJK development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".