Impact of an Augmented Reality Navigation System (SIRIO) on Bone Percutaneous Procedures: A Comparative Analysis with Standard CT-Guided Technique
Bibliographic record
Abstract
(1) Background: The purpose of this study is to evaluate the impact of an augmented reality navigation system (SIRIO) for percutaneous biopsies and ablative treatments on bone lesions, compared to a standard CT-guided technique. (2) Methods: Bioptic and ablative procedures on bone lesions were retrospectively analyzed. All procedures were divided into SIRIO and Non-SIRIO groups and in <2 cm and >2 cm groups. Number of CT-scans, procedural time and patient's radiation dose were reported for each group. Diagnostic accuracy was obtained for bioptic procedures. (3) Results: One-hundred-ninety-three procedures were evaluated: 142 biopsies and 51 ablations. Seventy-four biopsy procedures were performed using SIRIO and 68 under standard CT-guidance; 27 ablative procedures were performed using SIRIO and 24 under standard CT-guidance. A statistically significant reduction in the number of CT-scans, procedural time and radiation dose was observed for percutaneous procedures performed using SIRIO, in both <2 cm and >2 cm groups. The greatest difference in all variables examined was found for procedures performed on lesions <2 cm. Higher diagnostic accuracy was found for all SIRIO-assisted biopsies. No major or minor complications occurred in any procedures. (4) Conclusions: The use of SIRIO significantly reduces the number of CT-scans, procedural time and patient's radiation dose in CT-guided percutaneous bone procedures, particularly for lesions <2 cm. An improvement in diagnostic accuracy was also achieved in SIRIO-assisted biopsies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".