Mixed reality holograms for percutaneous lead extraction of cardiac implantable electronic devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract To assess the potential of mixed reality holograms (MixR) based on CT images to improve percutaneous lead extraction (PLE) planning and intraoperative assistance. This was a prospective, controlled, single-centre study. Five patients with CIED infection for PLE were included in the study. Conventional imaging (chest radiograph and CT) and MixR holograms were evaluated for preoperative planning to identify common complications such as vascular thrombosis, broken leads, loops, kinking, fibrosis along the wires, and perforation of cardiovascular structures. The degree of difficulty of the procedure was estimated based on potential complications. After the PLE procedure, the level of concordance between conventional imaging and MixR holograms with intraoperative findings was evaluated. The utility of MixR intraoperative guidance was also assessed. MixR holograms demonstrated a very high correlation in predicting the presence of loops, kinking, and fibrosis compared to conventional imaging, which showed a low-to-high correlation. MixR also showed a high correlation in estimating the degree of difficulty of the procedure compared to conventional imaging, which tended to underestimate it. The surgeon who performed the PLE agreed that MixR was helpful during intraoperative assistance. MixR holograms based on CT images are an effective tool for understanding cardiovascular anatomy and detecting potential areas of complications. MixR may be used as a complementary tool for both preoperative planning and intraoperative assistance in PLE procedures. Graphical abstract Mixed reality holograms for intraprocedural intervention assistance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it