Differences between Manufacturers of Computed Tomography–Based Computer-Assisted Surgery Systems Do Exist
Bibliographic record
Abstract
STUDY DESIGN: Literature review. OBJECTIVE: Several studies have shown that the accuracy of pedicle screw placement significantly improves with use of computed tomography (CT)-based navigation systems. Yet, there has been no systematic review directly comparing accuracy of pedicle screw placement between different CT-based navigation systems. The objective of this study is to review the results presented in the literature and compare CT-based navigation systems relative only to screw placement accuracy. METHODS: Data sources included CENTRAL, Medline, PubMed, and Embase databases. Studies included were randomized clinical trials, case series, and case-control trials reporting the accuracy of pedicle screws placement using CT-based navigation. Two independent reviewers extracted the data from the selected studies that met our inclusion criteria. Publications were grouped based on the CT-based navigation system used for pedicle screw placement. RESULTS: < 0.0001, 95% confidence interval 0.92 to 1.23) in accuracy of pedicle screw placement between three different CT-based navigation systems. The mean (weighted) accuracy of pedicle screws placement based on the CT-based navigation system was found to be 97.20 ± 2.1% in StealthStation (Medtronic, United States) and 96.1 ± 3.9% in VectorVision (BrainLab, Germany). CONCLUSION: This review summarizes results presented in the literature and compares screw placement accuracy using different CT-based navigation systems. Although certain factors such as the extent of the procedure and the experience and skills of the surgeon were not accounted for, the differences in accuracy demonstrated should be considered by spine surgeons and should be validated for effects on patients' outcome.
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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 | 0.030 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".