Alignment Goals in Adult Spinal Deformity Surgery
Bibliographic record
Abstract
Study DesignNarrative review.ObjectivesAdult spinal deformity (ASD) surgery has progressively transitioned from mean regional alignment targets to individualized segmental alignment goals, and from health-related quality of life (HRQL) alignment goals to the prevention of mechanical complications.MethodsNarrative review discussing sagittal alignment concepts and goals in ASD surgery.ResultsTraditional metrics for measuring sagittal spinal alignment such as pelvic incidence - lumbar lordosis (PI-LL), thoracic kyphosis, and sagittal vertical axis (SVA) may lack the specificity necessary for individualized alignment planning. Compensatory pelvic retroversion and knee flexion are critical determinants of maintaining the upright position. Research has been conflicting as to whether postoperative sagittal alignment is associated with improvements in HRQOL's. However, this may reflect a lack of sensitivity in the traditional alignment targets and PROM's measures, rather than a true lack of relationship between sagittal alignment and functional outcomes. Recent studies show that sagittal parameters have a limited impact on HRQL scores in non-operated patients, but significantly impact post-operative HRQOL measures and mechanical complications in patients treated with spinal fusion. Latest evidence suggests that compensatory mechanisms need to be eliminated and the ideal shape needs to be restored with surgery, to reduce postoperative mechanical complications. Multiple alignment strategies are proposed for that purpose.ConclusionsWhile best evidence shows an improvement in ASD alignment strategies over the last decade, mechanical failures and reoperations are still a cause for concern. This narrative review analyzes the strengths and weaknesses of the different alignment strategies and identifies the main areas of debate.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".