Complications with and without the Use of Computer-Assistance in Lumbar Fusion Surgery: Analysis of 15,222 Patients in ACS-NSQIP Database
Bibliographic record
Abstract
Introduction The objective of this paper is to demonstrate the difference in post-operative complication rates between Computer-assisted surgery (CAS) and conventional techniques in spine surgery. Several studies have shown that the accuracy of pedicle screw placement significantly improves with use of CAS. Yet, few studies have compared the incidence of post-operative complications between CAS and conventional techniques. Material and Methods The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was used to identify patients that underwent posterior lumbar fusion from 2011 to 2013. Multivariate analysis was conducted to demonstrate the difference in post-operative complication rates between CAS and conventional techniques in spine surgery. Results Out of 15,222 patients, 14,382 (95.1%) were operated with conventional techniques and 740 (4.90%) were operated with CAS. Multivariate analysis showed that patients in the CAS group had less odds to experience adverse events post-operatively (OR 0.57, p < 0.001). Minor adverse events occurred in 2905 (20.2%) patients in the conventional group and 98 (13.2%) patients in CAS group (OR=0.57, p < 0.001). Blood transfusion was present in 2488 (17.3%) of the patients in the conventional group compared with 98 of the patients in CAS group (10.95%)(OR=0.56, p < 0.001). The mean operative time in the conventional group was 205.2 ± 106.1 minutes, and 227.0 ± 111.9 minutes in the CAS group. This difference was statistically significant (r=20.14, p < 0.001). Conclusion This paper examined the complications in lumbar spinal surgery with or without the use of CAS. These results suggest that CAS may provide a safer technique for implant placement in lumbar fusion surgeries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".