Kyphoplasty interventions using a navigation system and C-arm CT data: first clinical results
Bibliographic record
Abstract
This study evaluates new applications using a novel navigation system with electromagnetic (EM) tracking in clinical routine. The navigation system (iGuide CAPPA, CAS innovations, Erlangen, Germany) consists of a PC with dedicated navigation software, the AURORA tracking system (NDI, Waterloo Ontario, Canada) and needles equipped with small coils in their tips for EM navigation. After patient positioning a 3D C-arm data set of the spine region of interest is acquired. The images are reconstructed and the 3D data set is directly transferred to the navigation system. Image loading and image to patient registration are performed automatically by the navigation system. For image acquisition a C-arm system with DynaCT option (AXIOM Artis, Siemens Healthcare, Forchheim, Germany) was used. As new clinical applications we performed kyphoplasty for reconstruction of collapsed vertebrae. All interventions were carried out without any complication. After a single planning scan the radiologists were able to place the needle in the designated vertebra. During needle driving 2D imaging was performed just in a few cases for control reasons. The time between planning and final needle positioning was reduced in all cases compared to conventional methods. Moreover, the number of control scans could be markedly reduced. The deviation of the needle to the planned target was less than 2 mm. The use of DynaCT images in combination with electromagnetic tracking-based navigation systems allows a precise needle positioning for kyphoplasty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".