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Record W4410333671 · doi:10.1007/s43390-025-01106-y

Development and validation of a pediatric spine surgical invasiveness index

2025· article· en· W4410333671 on OpenAlexaff
Vivien Chan, Adeesya Gausper, Andrew Chan-Tai-Kong, Andy M. Liu, Suhas K. Etigunta, Justin K. Scheer, Lindsay M. Andras, David L. Skaggs

Bibliographic record

VenueSpine Deformity · 2025
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
FundersCedars-Sinai Medical Center
KeywordsMedicineIndex (typography)SPINE (molecular biology)Orthopedic surgeryMedical physicsSurgeryBioinformaticsWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

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 There were 37,658 patients included (Derivation cohort: 26,372; Validation cohort: 11,286). In the linear regression analysis, more posterior fusion levels (7–12 levels: 0.54, p<0.001;>12 levels: 1.40, p<0.001), anterior fusion 1–3 levels (2.42, p<0.001), anterior fusion ≥4 levels (2.93, p<0.001), pelvic instrumentation (0.79, p<0.001), and previous spinal deformity surgery (0.44, p<0.001) were associated with longer operative time. Each level of posterior column osteotomy (0.13, p<0.001) and three-column osteotomy (0.61, p<0.001) were associated with increased operative time. Points were assigned to each surgical component: 7–12 posterior fusion levels (4 pts), >12 posterior fusion levels (11 pts), anterior fusion 1-3 levels (19 pts), anterior fusion ≥4 levels (23 pts), pelvic instrumentation (6 pts), previous spinal deformity surgery (3 pts), posterior column osteotomy (1 pt per level), and three-column osteotomy (5 pts per level). In the derivation cohort, each point was associated with an increase in operative time by 0.13 hours (R 2 =0.16, p<0.001). In the validation cohort, each point was associated with an increase in operative time by 0.12 hours (R 2 =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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.302
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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