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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".