Development and Validation of a Novel Adult Spinal Deformity Surgical Invasiveness Score: Analysis of 464 Patients
Bibliographic record
Abstract
BACKGROUND: A surgical invasiveness index (SII) has been validated in general spine procedures but not adult spinal deformity (ASD). OBJECTIVE: To assess the ability of the SII to determine the invasiveness of ASD surgery and to create and validate a novel ASD index incorporating deformity-specific factors, which could serve as a standardized metric to compare outcomes and risk stratification of different ASD procedures for a given deformity. METHODS: Four hundred sixty-four patients who underwent ASD surgery between 2009 and 2012 were identified in 2 multicenter prospective registries. Multivariable models of estimated blood loss (EBL) and operative time were created using deformity-specific factors. Beta coefficients derived from these models were used to attribute points to each component. Scoring was iteratively refined to determine the R2 value of multivariate models of EBL and operative time using adult spinal deformity-surgical (ASD-S) as an independent variable. Similarly, we determined weighting of postoperative changes in radiographical parameters, which were incorporated into another index (adult spinal deformity-surgical and radiographical [ASD-SR]). The ability of these models to predict surgical invasiveness was assessed in a validation cohort. RESULTS: Each index was a significant, independent predictor of EBL and operative time (P < .001). On multivariate analysis, ASD-S and ASD-SR explained more variability in EBL and operative time than did the SII (P < .001). The ASD-SR explained 21% of the variation in EBL and 10% of the variation in operative time, whereas the SII explained 17% and 3.2%, respectively. CONCLUSION: The ASD-SR, which incorporates deformity-specific components, more accurately predicts the magnitude of ASD surgery than does the SII.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".