Should Sagittal Spinal Alignment Targets for Adult Spinal Deformity Correction Depend on Pelvic Incidence and Age?
Bibliographic record
Abstract
STUDY DESIGN: Retrospective analysis. OBJECTIVE: Determine whether deformity corrections should vary by pelvic incidence (PI). SUMMARY OF BACKGROUND DATA: Alignment targets for deformity correction have been reported for various radiographic parameters. The T1 pelvic-angle (TPA) has gained in applications for adult spinal deformity (ASD) surgical-planning since it directly measures spinal alignment separate from pelvic- and lower-extremity compensation. Recent studies have demonstrated that ASD corrections should be age specific. METHODS: A prospective database of consecutive ASD patients was analyzed in conjunction with a normative spine database. Clinical measures of disability included the Oswestry Disability Index (ODI) and Short Form 36 Survey (SF-36) Physical Component Score (PCS). Baseline relationships between TPA, age, PI, and ODI/SF-36 PCS scores were analyzed in the ASD and asymptomatic patients. Linear regression modeling was used to determine alignment targets based on PI and age-specific normative SF-36-PCS values. RESULTS: Nine hundred three ASD patients (mean 53.7 yr) and 111 normative subjects (mean 50.7 yr) were included. Patients were subanalyzed by PI: low, medium, high (<40, 40-75, >75); and age: elderly (>65 yr, n = 375), middle age (45-65 yr, n = 387), and young (18-45 yr, n = 141). TPA and SRS-Schwab parameters correlated with age and PI in ASD and normative subjects (r = 0.42, P < 0.0001). ODI correlated with PCS (r = 0.71, P < 0.0001). Linear regression analysis using age-normative SF-36-PCS values demonstrated that ideal spinopelvic alignment is less strict with increasing PI and age. CONCLUSION: Targets for ASD correction should vary by age and PI. This is demonstrated in both asymptomatic and ASD subjects. Using age-normative SF-36 PCS values, alignment targets are described for different age and PI categories. High-PI patients do not require as rigorous realignments to attain age-specific normative levels of health status. As such, sagittal spinal alignment targets increase with increasing age as well as PI. LEVEL OF EVIDENCE: 3.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".