Use of Computer Assistance in Lumbar Fusion Surgery: Analysis of 15 222 Patients in the ACS-NSQIP Database
Bibliographic record
Abstract
Study Design: Retrospective cohort study. Objective: Several studies have shown that the accuracy of pedicle screw placement significantly improves with use of computer-assisted surgery (CAS). Yet few studies have compared the incidence of postoperative complications between CAS and conventional techniques. The objective of this study is to determine the difference in postoperative complication rates between CAS and conventional techniques in spine surgery. Methods: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was used to identify patients who underwent posterior lumbar fusion from 2011 to 2013. Multivariate analysis was conducted to demonstrate the difference in postoperative 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 fewer odds to experience adverse events postoperatively (odds ratio [OR] = 0.57, P < .001). Minor adverse events occurred in 2905 (20.2%) patients in the conventional group and in 98 (13.2%) patients in the CAS group (OR = 0.57, P < .001). Blood transfusion was present in 2488 (17.3%) of the patients in the conventional group compared to 81 (11.0%) of the patients in the CAS group (OR = 0.56, P < .001). The mean operative time in the conventional group was 205.2 ± 106.1 minutes, and it was 227.0 ± 111.9 minutes in the CAS group. This difference was statistically significant ( r = 20.14, P < .001). Conclusion: This article 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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".