Influence of correction objectives on the optimal scoliosis instrumentation strategy: A preliminary study
Bibliographic record
Abstract
In three recent studies we have shown how different correction objectives from a group of experienced spine surgeons add to the variability in AIS instrumentation strategies. This study examined the effect of correction objectives of three surgeons on the optimal instrumentation strategy. An optimization method using six instrumentation design parameters (e.g. limits of the instrumented segment, number, type and location of implants and rod shape) that were manipulated in a uniform experimental design framework was linked to a patient-specific biomechanical model to analyze the effects of a specific instrumentation configuration. The optimization cost function was formulated to maximize correction in the three anatomic planes and with minimal number of instrumented levels. Three surgeons from the Spinal Deformity Study Group provided their respective correction objectives for a single patient (56 degrees thoracic and 38 degrees lumbar Cobb angle). For each surgeon, 702 surgical configurations were iteratively simulated using a biomechanical model. The influence of the three different correction objectives on the optimal surgical strategy was evaluated. The resulting optimal fusion levels were T2-L4, T4-L2, and T4-L1. A Wilcoxon non parametric test analysis showed that fusion levels and the location of implants significantly were influenced by the correction objectives strategies (p<0.05). The optimal number of implants although different (12 vs.11 vs.10) was not statistically significant (p>0.1). Thus different surgeon-specified correction objectives produced different optimal instrumentation strategies for the same patient.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".