The Clinical Impact of Augmented Reality Surgical Navigation on Pedicle Screw Placement and its Effect on Perioperative Outcomes: A Systematic Review
Bibliographic record
Abstract
Background: Spinal fusion is a common form of orthopedic surgery, the most common of which involves pedicle screw placement (PSP). Despite well-documented benefits, pedicle screws are associated with several intraoperative complications. This area of surgery has subsequently been recipient to many surgical developments. Currently, augmented reality surgical navigation (ARSN) is at the forefront of surgical interest. This systematic review evaluates whether, when compared to freehand, fluoroscopic, and intraoperative image-guided navigation, ARSN results in superior screw accuracy and operative outcomes for patients undergoing PSP surgery. Methods: Data collection was performed on PubMed, Ovid MEDLINE, the Cochrane Library, Embase, and the Web of Science between January 7, 2023, and January 8, 2024. PRISMA guidelines were followed and the level of evidence was graded per the Centre for Evidence-Based Medicine's recommendations. Risk of bias was assessed per the ROBINS-I tool and the Cochrane guide for assessing study quality. A modified version of the Newcastle-Ottawa Scale was used to determine the certainty of the body of evidence. Results: A total of 521 papers were obtained from all bibliographical databases, 31 of which were included in the final review. ARSN resulted in a significantly greater number of screws placed as Gertzbein and Robbins grade 1 or 2 (93.33% vs 85.86%, p<0.000), significantly reduced intraoperative blood loss (470.32 vs 802.44 ml, p=0.050), comparative operative duration (281.6 vs 255.5 min, p=0.819), comparative time to place a screw (2.71 vs 3.1 min, p=0.703), and a nonsignificant reduction in hospital stay (5.4 vs 7.5 days, p=0.097). Maximum follow-up was more than 14 days. Conclusions: ARSN results in a significantly greater number of screws placed at Gertzbein-Robertson grade 1 or 2 than non-ARSN surgery. Therefore, ARSN can be considered as a safe and efficacious technical innovation within PSP surgery.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".