Augmented-reality–guided insertion of sliding hip screw guidewire: a preclinical investigation
Bibliographic record
Abstract
Background: The sliding hip screw (SHS) is frequently used in the management of hip fractures; successful placement depends on accurate positioning of the lag screw in the femoral head guided by fluoroscopy. We proposed to leverage the capabilities of augmented reality (AR) to overlay virtual images of the desired guidewire trajectory directly onto the surgical field to guide the surgeon during SHS guidewire insertion. Methods: Using a commercially available AR headset and software, we performed preprocedural planning using computed tomography scans to identify the optimal trajectory for SHS guidewire insertion in the neck of a Sawbones femur model. The images of the scanned femurs containing the virtual guidewire trajectory were overlaid on the physical models such that the user could see a composite view of the computer-generated images and the physical environment. Two second-year orthopedic residents each inserted 15 guidewires under AR guidance and 15 guidewires under fluoroscopy. Results: Of the 30 guidewires inserted under AR guidance, 24 (80%) were within the femoral neck, and 16 (53%) were fully enclosed within the femoral head. Nine (56%) of the 16 perforations were due to insertions that were too far along the planned trajectory. Thirteen (81%) of the successful attempts with AR had an appropriate position, compared to 25/26 (96%) with fluoroscopy. It took significantly less time to perform the procedure using fluoroscopy than AR (p < 0.05). Fluoroscopy required on average 18.7 shots. Conclusion: Augmented reality provides an opportunity to aid in guidewire insertion in a preplanned trajectory with less radiation exposure in a sterile environment, but technical challenges remain to be solved to enable widespread adoption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".