The use of three-dimensional navigation and advanced intraoperative imaging in minimally invasive pelvic and acetabular fracture fixation: A systematic review
Bibliographic record
Abstract
Pelvic and acetabular fractures are challenging injuries to treat. This review evaluates three-dimensional intraoperative imaging and navigation-guided percutaneous SI, trans-iliac, trans-sacral, and acetabular screw placement versus conventional methods performed with C-arm imaging. A systematic search of MEDLINE, Embase, and Cochrane was performed. Two reviewers independently extracted data into a collaborative data form designed a priori and piloted prior to its use. Descriptive statistics are presented where applicable. Summary statistics analysis was presented based on the fracture type. Continuous data such as fluoroscopic and operative time were compared with unpaired Student t-test and pooled data of revision rate and complications were compared with chi-square analysis with an alpha set at 0.05. The rate of complications using conventional fluoroscopy was 11.3% (26/230) compared to three-dimensional navigation (6.7% (40/597), X 2 (DF: 1, N = 827) = 4.79, p = .028.) which translated to a higher rate of revision surgeries in the conventional fluoroscopy group (10.9% vs. 0.8%) X 2 (DF: 1, N = 827) = 47.8, p ≤.001. Average fluoroscopic time was lower for studies using three-dimensional navigation (28.8 ± 14.3 s, n = 71) compared to conventional fluoroscopy (57.8 ± 4.2 s, n = 38, p ≤.001). Three-dimensional navigation during minimally invasive pelvis and acetabular fracture fixation may have some benefits. Level of evidence: IV.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".