Are We Focused on the Wrong Early Postoperative Quality Metrics? Optimal Realignment Outweighs Perioperative Risk in Adult Spinal Deformity Surgery
Bibliographic record
Abstract
Background: While reimbursement is centered on 90-day outcomes, many patients may still achieve optimal, long-term outcomes following adult spinal deformity (ASD) surgery despite transient short-term complications. Objective: Compare long-term clinical success and cost-utility between patients achieving optimal realignment and suboptimally aligned peers. Study Design/Setting: Retrospective cohort study of a prospectively collected multicenter database. Methods: ASD patients with two-year (2Y) data included. Groups were propensity score matched (PSM) for age, frailty, body mass index (BMI), Charlson Comorbidity Index (CCI), and baseline deformity. Optimal radiographic criteria are defined as meeting low deformity in all three (Scoliosis Research Society) SRS-Schwab parameters or being proportioned in Global Alignment and Proportionality (GAP). Cost-per-QALY was calculated for each time point. Multivariable logistic regression analysis and ANCOVA (analysis of covariance) adjusting for baseline disability and deformity (pelvic incidence (PI), pelvic incidence minus lumbar lordosis (PI-LL)) were used to determine the significance of surgical details, complications, clinical outcomes, and cost-utility. Results: A total of 930 patients were considered. Following PSM, 253 “optimal” (O) and 253 “not optimal” (NO) patients were assessed. The O group underwent more invasive procedures and had more levels fused. Analysis of complications by two years showed that the O group suffered less overall major (38% vs. 52%, p = 0.021) and major mechanical complications (12% vs. 22%, p = 0.002), and less reoperations (23% vs. 33%, p = 0.008). Adjusted analysis revealed O patients more often met MCID (minimal clinically important difference) in SF-36 PCS, SRS-22 Pain, and Appearance. Cost-utility-adjusted analysis determined that the O group generated better cost-utility by one year and maintained lower overall cost and costs per QALY (both p < 0.001) at two years. Conclusions: Fewer late complications (mechanical and reoperations) are seen in optimally aligned patients, leading to better long-term cost-utility overall. Therefore, the current focus on avoiding short-term complications may be counterproductive, as achieving optimal surgical correction is critical for long-term success.
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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 | 0.006 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".