Calibration of a comprehensive predictive model for the development of proximal junctional kyphosis and failure in adult spinal deformity patients with consideration of contemporary goals and techniques
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to calibrate an updated predictive model incorporating novel clinical, radiographic, and prophylactic measures to assess the risk of proximal junctional kyphosis (PJK) and failure (PJF). METHODS: Operative patients with adult spinal deformity (ASD) and baseline and 2-year postoperative data were included. PJK was defined as ≥ 10° in sagittal Cobb angle between the inferior uppermost instrumented vertebra (UIV) endplate and superior endplate of the UIV + 2 vertebrae. PJF was radiographically defined as a proximal junctional sagittal Cobb angle ≥ 15° with the presence of structural failure and/or mechanical instability, or PJK with reoperation. Backstep conditional binary supervised learning models assessed baseline demographic, clinical, and surgical information to predict the occurrence of PJK and PJF. Internal cross validation of the model was performed via a 70%/30% cohort split. Conditional inference tree analysis determined thresholds at an alpha level of 0.05. RESULTS: Seven hundred seventy-nine patients with ASD (mean 59.87 ± 14.24 years, 78% female, mean BMI 27.78 ± 6.02 kg/m2, mean Charlson Comorbidity Index 1.74 ± 1.71) were included. PJK developed in 50.2% of patients, and 10.5% developed PJF by their last recorded visit. The six most significant demographic, radiographic, surgical, and postoperative predictors of PJK/PJF were baseline age ≥ 74 years, baseline sagittal age-adjusted score (SAAS) T1 pelvic angle modifier > 1, baseline SAAS pelvic tilt modifier > 0, levels fused > 10, nonuse of prophylaxis measures, and 6-week SAAS pelvic incidence minus lumbar lordosis modifier > 1 (all p < 0.015). Overall, the model was deemed significant (p < 0.001), and internally validated receiver operating characteristic analysis returned an area under the curve of 0.923, indicating robust model fit. CONCLUSIONS: PJK and PJF remain critical concerns in ASD surgery, and efforts to reduce the occurrence of PJK and PJF have resulted in the development of novel prophylactic techniques and enhanced clinical and radiographic selection criteria. This study demonstrates a validated model incorporating such techniques that may allow for the prediction of clinically significant PJK and PJF, and thus assist in optimizing patient selection, enhancing intraoperative decision making, and reducing postoperative complications in ASD surgery.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".