Proximal Junctional Kyphosis and Failure Prophylaxis Improves Cost Efficacy, While Maintaining Optimal Alignment, in Adult Spinal Deformity Surgery
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To investigate the cost-effectiveness and impact of prophylactic techniques on the development of proximal junctional kyphosis (PJK) and proximal junctional failure (PJF) in the context of postoperative alignment. METHODS: Adult spinal deformity patients with fusion to pelvis and 2-year data were included. Patients receiving PJK prophylaxis (hook, tether, cement, minimally-invasive surgery approach) were compared to those who did not. These cohorts were further stratified into "Matched" and "Unmatched" groups based on achievement of age-adjusted alignment criteria. Costs were calculated using the Diagnosis-Related Group costs accounting for PJK prophylaxis, postoperative complications, outpatient health care encounters, revisions, and medical-related readmissions. Quality-adjusted life years were calculated using Short Form-36 converted to Short-Form Six-Dimension (SF-6D) and used an annual 3% discount rate. Multivariate analysis controlling for age, sex, levels fused, and baseline deformity severity assessed outcomes of developing PJK/PJF if matched and/or with use of PJK prophylaxis. RESULTS: A total of 738 adult spinal deformity patients met inclusion criteria (age: 63.9 ± 9.9, body mass index: 28.5 ± 5.7, Charlson comorbidity index: 2.0 ± 1.7). Multivariate analysis revealed patients corrected to age-adjusted criteria postoperatively had lower rates of developing PJK or PJF (odds ratio [OR]: 0.4, [0.2-0.8]; P = .011) with the use of prophylaxis. Among those unmatched in T1 pelvic angle, pelvic incidence lumbar lordosis mismatch, and pelvic tilt, prophylaxis reduced the likelihood of developing PJK (OR: 0.5, [0.3-0.9]; P = .023) and PJF (OR: 0.1, [0.03-0.5]; P = .004). Analysis of covariance analysis revealed patients matched in age-adjusted alignment had better cost-utility at 2 years compared with those without prophylaxis ($361 539.25 vs $419 919.43; P < .001). Patients unmatched in age-adjusted criteria also generated better cost ($88 348.61 vs $101 318.07; P = .005) and cost-utility ($450 190.80 vs $564 108.86; P < .001) with use of prophylaxis. CONCLUSION: Despite additional surgical cost, the optimization of radiographic realignment in conjunction with prophylaxis of the proximal junction appeared to be a more cost-effective strategy, primarily because of the minimization of reoperations secondary to mechanical failure. Even among those not achieving optimal alignment, junctional prophylactic measures were shown to improve cost efficiency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".