Development and validation of a pediatric spine surgical invasiveness index
Bibliographic record
Abstract
PURPOSE: Surgical invasiveness indices have been used in adult spine surgery to characterize the invasiveness of complex procedures and for risk stratification. This has not been studied in the pediatric population. The purpose of this study was to develop and validate a surgical invasiveness index for pediatric spinal deformity surgery. METHODS: The National Surgical Quality Improvement Program (NSQIP) Pediatric database was queried between the years 2016-2022. Patients were included if they were <18 years of age, received posterior or anterior-posterior spinal fusion surgery, and had a diagnosis of spinal deformity. The study cohort was divided into a derivation cohort and a validation cohort. A multivariable linear regression analysis was performed to identify surgical components associated with operative time. Surgical components of interest included number of posterior fusion levels, number of anterior fusion levels, pelvic instrumentation, posterior column osteotomies, three-column osteotomies, and prior spinal deformity surgery. Statistically significant variables were used to establish a pediatric spinal deformity surgical invasiveness index. The score was assessed and validated using linear and logistic regression analysis and receiver operating characteristic curve analysis on operative time and allogeneic transfusion. RESULTS: =0.15, p<0.001). In the derivation cohort, the area under the curve (AUC) for operative time ≥8 hours and allogeneic transfusion were 0.74 and 0.71, respectively. In the validation cohort, the AUC for operative time ≥8 hours and allogeneic transfusion were 0.74 and 0.70, respectively. CONCLUSION: A pediatric spinal deformity surgical invasiveness index was created and predictive of prolonged operative time and allogeneic transfusion. This is the first quantitative tool to measure the extent of surgical interventions in pediatric spine surgery.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".