How Good Are Surgeons at Achieving Their Preoperative Goal Sagittal Alignment Following Adult Deformity Surgery?
Bibliographic record
Abstract
Study Design Multicenter, prospective cohort Objectives Malalignment following adult spine deformity (ASD) surgery can impact outcomes and increase mechanical complications. We assess whether preoperative goals for sagittal alignment following ASD surgery are achieved. Methods ASD patients were prospectively enrolled based on 3 criteria: deformity severity (PI-LL ≥25°, TPA ≥30°, SVA ≥15 cm, TCobb≥70° or TLCobb≥50°), procedure complexity (≥12 levels fused, 3-CO or ACR) and/or age (>65 and ≥7 levels fused). The surgeon documented sagittal alignment goals prior to surgery. Goals were compared with achieved alignment on first follow-up standing radiographs. Results The 266 enrolled patients had a mean age of 61.0 years (SD = 14.6) and 68% were women. Mean instrumented levels was 13.6 (SD = 3.8), and 23.2% had a 3-CO. Mean (SD) offsets (achieved-goal) were: SVA = −8.5 mm (45.6 mm), PI-LL = −4.6° (14.6°), TK = 7.2° (14.7°), reflecting tendencies to undercorrect SVA and PI-LL and increase TK. Goals were achieved for SVA, PI-LL, and TK in 74.4%, 71.4%, and 68.8% of patients, respectively, and was achieved for all 3 parameters in 37.2% of patients. Three factors were independently associated with achievement of all 3 alignment goals: use of PACs/equivalent for surgical planning ( P < .001), lower baseline GCA ( P = .009), and surgery not including a 3-CO ( P = .037). Conclusions Surgeons failed to achieve goal alignment of each sagittal parameter in ∼25-30% of ASD patients. Goal alignment for all 3 parameters was only achieved in 37.2% of patients. Those at greatest risk were patients with more severe deformity. Advancements are needed to enable more consistent translation of preoperative alignment goals to the operating room.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".