Intraoperative Cone Beam CT (O-Arm) and Stereotactic Navigation (StealthStation) System in Complex Adult Spine Surgery: Early Experience and Learning Curve
Bibliographic record
Abstract
Introduction Computer-assisted navigation and intraoperative imaging systems have been shown to improve pedicle screw accuracy in a variety of instrumented spine surgeries. There is limited data evaluating the clinical learning curve for surgeons and its relationship to patient outcomes when using intraoperative navigation and imaging systems. We examined the clinical learning curve and patient outcomes of using O-Arm and StealthStation for six fellowship-trained spine surgeons at our institution, a single quaternary referral center, from 2009 to 2013. Materials and Methods This ambispective study examined 231 surgical cases where O-arm and StealthStation were used to facilitate pedicle instrumentation. The learning curve was determined by examining the year-by-year total operative time and blood loss, operative time and blood loss per surgical level, and the incidence of surgery-related adverse events. Adverse events were prospectively collected using the spine adverse events severity system (SAVES). Results O-arm and StealthStation were acquired at our institution in late 2008 and all spine surgeons were using this navigation system by the beginning of 2009. A total of 231 patients had screws placed using the O-arm and StealthStation between January 1, 2009 and December 31, 2012. There were 430 screws placed in 27 cases in 2009, 556 screws in 54 cases in 2010, 674 screws in 59 cases in 2011, and 758 screws in 75 cases in 2012, p < 0.05. The average estimated blood loss (EBL) decreased from 1,229 mL in 2009 to 907 mL in 2012, p < 0.05. The EBL per case per number of levels instrumented decreased from 5.72 mL in 2008 to 2.39 mL in 2012, p < 0.05. Mean operating time decreased from 407 minutes to 378 minutes from 2009 to 2012, p < 0.05. The number of misplaced screws per case decreased from 0.78 to 0.54 from 2009 to 2012, p < 0.05. There were no significant differences in incidences of dural tear, surgical site infection, or other surgical adverse events during the study period. Conclusion Our results demonstrate that there is a learning curve to the use of intraoperative CT-based navigation, as measured by OR time, intraoperative blood loss, and screw malposition. There were no significant differences in surgical adverse events during this learning period.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".