Predicting Mechanical Failure Following Cervical Deformity Surgery: A Composite Score Integrating Age-Adjusted Cervical Alignment Targets
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cohort study. OBJECTIVES: Investigate a composite score to evaluate the relationship between alignment proportionality and risk of distal junctional kyphosis (DJK). METHODS: 84 patients with minimum 1 year follow-up were included (age = 61.1 ± 10.3 years, 64.3% women). The Cervical Score was constructed using offsets from age-adjusted normative values for sagittal vertical axis (SVA), T1 Slope (TS), and TS minus cervical lordosis (CL). Individual points were assigned based on offset with age-adjusted alignment targets and summed to generate the Cervical Score. Rates of mechanical failure (DJK revision or severe DJK [DJK> 20° and ΔDJK> 10°]) were assessed overall and based on Cervical Score. Logistical regressions assessed associations between early radiographic alignment and 1-year failure rate. RESULTS: Mechanical failure rate was 21.4% (N = 18), 10.7% requiring revision. By multivariate logistical regression: 3-month T1S (OR: .935), TS-CL (OR:0.882), and SVA (OR:1.015) were independent predictors of 1-year failure (all P < .05). Cervical Score ranged (-6 to 6), 37.8% of patients between -1 and 1, and 50.0% with 2 or higher. DJK patients had significantly higher Cervical Score (4.1 ± 1.3 vs .6 ± 2.2, P < .001). Patients with a score ≥3 were significantly more likely to develop a failure (71.4%) with OR of 38.55 (95%CI [7.73; 192.26]) and Nagelkerke r2 .524 (P < .001). CONCLUSION: This study developed a composite alignment score predictive of mechanical failures in CD surgery. A score ≥3 at 3 months following surgery was associated with a marked increase in failure rate. The Cervical Score can be used to analyze sagittal alignment and help define realignment objectives to reduce mechanical failure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".